import os
import pandas as pd
import numpy as np
import sklearn
import matplotlib.pyplot as plt
import seaborn as sns
import plotly.graph_objects as go
from IPython import display
from sklearn.impute import SimpleImputer
from sklearn.preprocessing import LabelEncoder, MinMaxScaler

from sklearn.linear_model import LogisticRegression
from sklearn.metrics import classification_report, accuracy_score, confusion_matrix
from sklearn.ensemble import RandomForestClassifier

from scipy.stats import f_oneway
from scipy.stats import chi2_contingency
import warnings
warnings.simplefilter(action="ignore", category=FutureWarning)

pd.set_option('display.max_columns', 12)
pd.set_option('display.max_rows', 1000)

Loading training data from the CSV

#Importing the dataset
dataset = pd.read_csv('heart.csv')
df = pd.DataFrame(dataset)
df

	Age	Sex	ChestPainType	RestingBP	Cholesterol	FastingBS	RestingECG	MaxHR	ExerciseAngina	Oldpeak	ST_Slope	HeartDisease
0	40	M	ATA	140	289	0	Normal	172	N	0.0	Up	0
1	49	F	NAP	160	180	0	Normal	156	N	1.0	Flat	1
2	37	M	ATA	130	283	0	ST	98	N	0.0	Up	0
3	48	F	ASY	138	214	0	Normal	108	Y	1.5	Flat	1
4	54	M	NAP	150	195	0	Normal	122	N	0.0	Up	0
5	39	M	NAP	120	339	0	Normal	170	N	0.0	Up	0
6	45	F	ATA	130	237	0	Normal	170	N	0.0	Up	0
7	54	M	ATA	110	208	0	Normal	142	N	0.0	Up	0
8	37	M	ASY	140	207	0	Normal	130	Y	1.5	Flat	1
9	48	F	ATA	120	284	0	Normal	120	N	0.0	Up	0
10	37	F	NAP	130	211	0	Normal	142	N	0.0	Up	0
11	58	M	ATA	136	164	0	ST	99	Y	2.0	Flat	1
12	39	M	ATA	120	204	0	Normal	145	N	0.0	Up	0
13	49	M	ASY	140	234	0	Normal	140	Y	1.0	Flat	1
14	42	F	NAP	115	211	0	ST	137	N	0.0	Up	0
15	54	F	ATA	120	273	0	Normal	150	N	1.5	Flat	0
16	38	M	ASY	110	196	0	Normal	166	N	0.0	Flat	1
17	43	F	ATA	120	201	0	Normal	165	N	0.0	Up	0
18	60	M	ASY	100	248	0	Normal	125	N	1.0	Flat	1
19	36	M	ATA	120	267	0	Normal	160	N	3.0	Flat	1
20	43	F	TA	100	223	0	Normal	142	N	0.0	Up	0
21	44	M	ATA	120	184	0	Normal	142	N	1.0	Flat	0
22	49	F	ATA	124	201	0	Normal	164	N	0.0	Up	0
23	44	M	ATA	150	288	0	Normal	150	Y	3.0	Flat	1
24	40	M	NAP	130	215	0	Normal	138	N	0.0	Up	0
25	36	M	NAP	130	209	0	Normal	178	N	0.0	Up	0
26	53	M	ASY	124	260	0	ST	112	Y	3.0	Flat	0
27	52	M	ATA	120	284	0	Normal	118	N	0.0	Up	0
28	53	F	ATA	113	468	0	Normal	127	N	0.0	Up	0
29	51	M	ATA	125	188	0	Normal	145	N	0.0	Up	0
30	53	M	NAP	145	518	0	Normal	130	N	0.0	Flat	1
31	56	M	NAP	130	167	0	Normal	114	N	0.0	Up	0
32	54	M	ASY	125	224	0	Normal	122	N	2.0	Flat	1
33	41	M	ASY	130	172	0	ST	130	N	2.0	Flat	1
34	43	F	ATA	150	186	0	Normal	154	N	0.0	Up	0
35	32	M	ATA	125	254	0	Normal	155	N	0.0	Up	0
36	65	M	ASY	140	306	1	Normal	87	Y	1.5	Flat	1
37	41	F	ATA	110	250	0	ST	142	N	0.0	Up	0
38	48	F	ATA	120	177	1	ST	148	N	0.0	Up	0
39	48	F	ASY	150	227	0	Normal	130	Y	1.0	Flat	0
40	54	F	ATA	150	230	0	Normal	130	N	0.0	Up	0
41	54	F	NAP	130	294	0	ST	100	Y	0.0	Flat	1
42	35	M	ATA	150	264	0	Normal	168	N	0.0	Up	0
43	52	M	NAP	140	259	0	ST	170	N	0.0	Up	0
44	43	M	ASY	120	175	0	Normal	120	Y	1.0	Flat	1
45	59	M	NAP	130	318	0	Normal	120	Y	1.0	Flat	0
46	37	M	ASY	120	223	0	Normal	168	N	0.0	Up	0
47	50	M	ATA	140	216	0	Normal	170	N	0.0	Up	0
48	36	M	NAP	112	340	0	Normal	184	N	1.0	Flat	0
49	41	M	ASY	110	289	0	Normal	170	N	0.0	Flat	1
50	50	M	ASY	130	233	0	Normal	121	Y	2.0	Flat	1
51	47	F	ASY	120	205	0	Normal	98	Y	2.0	Flat	1
52	45	M	ATA	140	224	1	Normal	122	N	0.0	Up	0
53	41	F	ATA	130	245	0	Normal	150	N	0.0	Up	0
54	52	F	ASY	130	180	0	Normal	140	Y	1.5	Flat	0
55	51	F	ATA	160	194	0	Normal	170	N	0.0	Up	0
56	31	M	ASY	120	270	0	Normal	153	Y	1.5	Flat	1
57	58	M	NAP	130	213	0	ST	140	N	0.0	Flat	1
58	54	M	ASY	150	365	0	ST	134	N	1.0	Up	0
59	52	M	ASY	112	342	0	ST	96	Y	1.0	Flat	1
60	49	M	ATA	100	253	0	Normal	174	N	0.0	Up	0
61	43	F	NAP	150	254	0	Normal	175	N	0.0	Up	0
62	45	M	ASY	140	224	0	Normal	144	N	0.0	Up	0
63	46	M	ASY	120	277	0	Normal	125	Y	1.0	Flat	1
64	50	F	ATA	110	202	0	Normal	145	N	0.0	Up	0
65	37	F	ATA	120	260	0	Normal	130	N	0.0	Up	0
66	45	F	ASY	132	297	0	Normal	144	N	0.0	Up	0
67	32	M	ATA	110	225	0	Normal	184	N	0.0	Up	0
68	52	M	ASY	160	246	0	ST	82	Y	4.0	Flat	1
69	44	M	ASY	150	412	0	Normal	170	N	0.0	Up	0
70	57	M	ATA	140	265	0	ST	145	Y	1.0	Flat	1
71	44	M	ATA	130	215	0	Normal	135	N	0.0	Up	0
72	52	M	ASY	120	182	0	Normal	150	N	0.0	Flat	1
73	44	F	ASY	120	218	0	ST	115	N	0.0	Up	0
74	55	M	ASY	140	268	0	Normal	128	Y	1.5	Flat	1
75	46	M	NAP	150	163	0	Normal	116	N	0.0	Up	0
76	32	M	ASY	118	529	0	Normal	130	N	0.0	Flat	1
77	35	F	ASY	140	167	0	Normal	150	N	0.0	Up	0
78	52	M	ATA	140	100	0	Normal	138	Y	0.0	Up	0
79	49	M	ASY	130	206	0	Normal	170	N	0.0	Flat	1
80	55	M	NAP	110	277	0	Normal	160	N	0.0	Up	0
81	54	M	ATA	120	238	0	Normal	154	N	0.0	Up	0
82	63	M	ASY	150	223	0	Normal	115	N	0.0	Flat	1
83	52	M	ATA	160	196	0	Normal	165	N	0.0	Up	0
84	56	M	ASY	150	213	1	Normal	125	Y	1.0	Flat	1
85	66	M	ASY	140	139	0	Normal	94	Y	1.0	Flat	1
86	65	M	ASY	170	263	1	Normal	112	Y	2.0	Flat	1
87	53	F	ATA	140	216	0	Normal	142	Y	2.0	Flat	0
88	43	M	TA	120	291	0	ST	155	N	0.0	Flat	1
89	55	M	ASY	140	229	0	Normal	110	Y	0.5	Flat	0
90	49	F	ATA	110	208	0	Normal	160	N	0.0	Up	0
91	39	M	ASY	130	307	0	Normal	140	N	0.0	Up	0
92	52	F	ATA	120	210	0	Normal	148	N	0.0	Up	0
93	48	M	ASY	160	329	0	Normal	92	Y	1.5	Flat	1
94	39	F	NAP	110	182	0	ST	180	N	0.0	Up	0
95	58	M	ASY	130	263	0	Normal	140	Y	2.0	Flat	1
96	43	M	ATA	142	207	0	Normal	138	N	0.0	Up	0
97	39	M	NAP	160	147	1	Normal	160	N	0.0	Up	0
98	56	M	ASY	120	85	0	Normal	140	N	0.0	Up	0
99	41	M	ATA	125	269	0	Normal	144	N	0.0	Up	0
100	65	M	ASY	130	275	0	ST	115	Y	1.0	Flat	1
101	51	M	ASY	130	179	0	Normal	100	N	0.0	Up	0
102	40	F	ASY	150	392	0	Normal	130	N	2.0	Flat	1
103	40	M	ASY	120	466	1	Normal	152	Y	1.0	Flat	1
104	46	M	ASY	118	186	0	Normal	124	N	0.0	Flat	1
105	57	M	ATA	140	260	1	Normal	140	N	0.0	Up	0
106	48	F	ASY	120	254	0	ST	110	N	0.0	Up	0
107	34	M	ATA	150	214	0	ST	168	N	0.0	Up	0
108	50	M	ASY	140	129	0	Normal	135	N	0.0	Up	0
109	39	M	ATA	190	241	0	Normal	106	N	0.0	Up	0
110	59	F	ATA	130	188	0	Normal	124	N	1.0	Flat	0
111	57	M	ASY	150	255	0	Normal	92	Y	3.0	Flat	1
112	47	M	ASY	140	276	1	Normal	125	Y	0.0	Up	0
113	38	M	ATA	140	297	0	Normal	150	N	0.0	Up	0
114	49	F	NAP	130	207	0	ST	135	N	0.0	Up	0
115	33	F	ASY	100	246	0	Normal	150	Y	1.0	Flat	1
116	38	M	ASY	120	282	0	Normal	170	N	0.0	Flat	1
117	59	F	ASY	130	338	1	ST	130	Y	1.5	Flat	1
118	35	F	TA	120	160	0	ST	185	N	0.0	Up	0
119	34	M	TA	140	156	0	Normal	180	N	0.0	Flat	1
120	47	F	NAP	135	248	1	Normal	170	N	0.0	Flat	1
121	52	F	NAP	125	272	0	Normal	139	N	0.0	Up	0
122	46	M	ASY	110	240	0	ST	140	N	0.0	Up	0
123	58	F	ATA	180	393	0	Normal	110	Y	1.0	Flat	1
124	58	M	ATA	130	230	0	Normal	150	N	0.0	Up	0
125	54	M	ATA	120	246	0	Normal	110	N	0.0	Up	0
126	34	F	ATA	130	161	0	Normal	190	N	0.0	Up	0
127	48	F	ASY	108	163	0	Normal	175	N	2.0	Up	0
128	54	F	ATA	120	230	1	Normal	140	N	0.0	Up	0
129	42	M	NAP	120	228	0	Normal	152	Y	1.5	Flat	0
130	38	M	NAP	145	292	0	Normal	130	N	0.0	Up	0
131	46	M	ASY	110	202	0	Normal	150	Y	0.0	Flat	1
132	56	M	ASY	170	388	0	ST	122	Y	2.0	Flat	1
133	56	M	ASY	150	230	0	ST	124	Y	1.5	Flat	1
134	61	F	ASY	130	294	0	ST	120	Y	1.0	Flat	0
135	49	M	NAP	115	265	0	Normal	175	N	0.0	Flat	1
136	43	F	ATA	120	215	0	ST	175	N	0.0	Up	0
137	39	M	ATA	120	241	0	ST	146	N	2.0	Up	0
138	54	M	ASY	140	166	0	Normal	118	Y	0.0	Flat	1
139	43	M	ASY	150	247	0	Normal	130	Y	2.0	Flat	1
140	52	M	ASY	160	331	0	Normal	94	Y	2.5	Flat	1
141	50	M	ASY	140	341	0	ST	125	Y	2.5	Flat	1
142	47	M	ASY	160	291	0	ST	158	Y	3.0	Flat	1
143	53	M	ASY	140	243	0	Normal	155	N	0.0	Up	0
144	56	F	ATA	120	279	0	Normal	150	N	1.0	Flat	1
145	39	M	ASY	110	273	0	Normal	132	N	0.0	Up	0
146	42	M	ATA	120	198	0	Normal	155	N	0.0	Up	0
147	43	F	ATA	120	249	0	ST	176	N	0.0	Up	0
148	50	M	ATA	120	168	0	Normal	160	N	0.0	Up	0
149	54	M	ASY	130	603	1	Normal	125	Y	1.0	Flat	1
150	39	M	ATA	130	215	0	Normal	120	N	0.0	Up	0
151	48	M	ATA	100	159	0	Normal	100	N	0.0	Up	0
152	40	M	ATA	130	275	0	Normal	150	N	0.0	Up	0
153	55	M	ASY	120	270	0	Normal	140	N	0.0	Up	0
154	41	M	ATA	120	291	0	ST	160	N	0.0	Up	0
155	56	M	ASY	155	342	1	Normal	150	Y	3.0	Flat	1
156	38	M	ASY	110	190	0	Normal	150	Y	1.0	Flat	1
157	49	M	ASY	140	185	0	Normal	130	N	0.0	Up	0
158	44	M	ASY	130	290	0	Normal	100	Y	2.0	Flat	1
159	54	M	ATA	160	195	0	ST	130	N	1.0	Up	0
160	59	M	ASY	140	264	1	LVH	119	Y	0.0	Flat	1
161	49	M	ASY	128	212	0	Normal	96	Y	0.0	Flat	1
162	47	M	ATA	160	263	0	Normal	174	N	0.0	Up	0
163	42	M	ATA	120	196	0	Normal	150	N	0.0	Up	0
164	52	F	ATA	140	225	0	Normal	140	N	0.0	Up	0
165	46	M	TA	140	272	1	Normal	175	N	2.0	Flat	1
166	50	M	ASY	140	231	0	ST	140	Y	5.0	Flat	1
167	48	M	ATA	140	238	0	Normal	118	N	0.0	Up	0
168	58	M	ASY	135	222	0	Normal	100	N	0.0	Up	0
169	58	M	NAP	140	179	0	Normal	160	N	0.0	Up	0
170	29	M	ATA	120	243	0	Normal	160	N	0.0	Up	0
171	40	M	NAP	140	235	0	Normal	188	N	0.0	Up	0
172	53	M	ATA	140	320	0	Normal	162	N	0.0	Up	0
173	49	M	NAP	140	187	0	Normal	172	N	0.0	Up	0
174	52	M	ASY	140	266	0	Normal	134	Y	2.0	Flat	1
175	43	M	ASY	140	288	0	Normal	135	Y	2.0	Flat	1
176	54	M	ASY	140	216	0	Normal	105	N	1.5	Flat	1
177	59	M	ATA	140	287	0	Normal	150	N	0.0	Up	0
178	37	M	NAP	130	194	0	Normal	150	N	0.0	Up	0
179	46	F	ASY	130	238	0	Normal	90	N	0.0	Up	0
180	52	M	ASY	130	225	0	Normal	120	Y	2.0	Flat	1
181	51	M	ATA	130	224	0	Normal	150	N	0.0	Up	0
182	52	M	ASY	140	404	0	Normal	124	Y	2.0	Flat	1
183	46	M	ASY	110	238	0	ST	140	Y	1.0	Flat	0
184	54	F	ATA	160	312	0	Normal	130	N	0.0	Up	0
185	58	M	NAP	160	211	1	ST	92	N	0.0	Flat	1
186	58	M	ATA	130	251	0	Normal	110	N	0.0	Up	0
187	41	M	ASY	120	237	1	Normal	138	Y	1.0	Flat	1
188	50	F	ASY	120	328	0	Normal	110	Y	1.0	Flat	0
189	53	M	ASY	180	285	0	ST	120	Y	1.5	Flat	1
190	46	M	ASY	180	280	0	ST	120	N	0.0	Up	0
191	50	M	ATA	170	209	0	ST	116	N	0.0	Up	0
192	48	M	ATA	130	245	0	Normal	160	N	0.0	Up	0
193	45	M	NAP	135	192	0	Normal	110	N	0.0	Up	0
194	41	F	ATA	125	184	0	Normal	180	N	0.0	Up	0
195	62	F	TA	160	193	0	Normal	116	N	0.0	Up	0
196	49	M	ASY	120	297	0	Normal	132	N	1.0	Flat	0
197	42	M	ATA	150	268	0	Normal	136	N	0.0	Up	0
198	53	M	ASY	120	246	0	Normal	116	Y	0.0	Flat	1
199	57	F	TA	130	308	0	Normal	98	N	1.0	Flat	0
200	47	M	TA	110	249	0	Normal	150	N	0.0	Up	0
201	46	M	NAP	120	230	0	Normal	150	N	0.0	Up	0
202	42	M	NAP	160	147	0	Normal	146	N	0.0	Up	0
203	31	F	ATA	100	219	0	ST	150	N	0.0	Up	0
204	56	M	ATA	130	184	0	Normal	100	N	0.0	Up	0
205	50	M	ASY	150	215	0	Normal	140	Y	0.0	Up	0
206	35	M	ATA	120	308	0	LVH	180	N	0.0	Up	0
207	35	M	ATA	110	257	0	Normal	140	N	0.0	Flat	1
208	28	M	ATA	130	132	0	LVH	185	N	0.0	Up	0
209	54	M	ASY	125	216	0	Normal	140	N	0.0	Flat	1
210	48	M	ASY	106	263	1	Normal	110	N	0.0	Flat	1
211	50	F	NAP	140	288	0	Normal	140	Y	0.0	Flat	1
212	56	M	NAP	130	276	0	Normal	128	Y	1.0	Up	0
213	56	F	NAP	130	219	0	ST	164	N	0.0	Up	0
214	47	M	ASY	150	226	0	Normal	98	Y	1.5	Flat	1
215	30	F	TA	170	237	0	ST	170	N	0.0	Up	0
216	39	M	ASY	110	280	0	Normal	150	N	0.0	Flat	1
217	54	M	NAP	120	217	0	Normal	137	N	0.0	Up	0
218	55	M	ATA	140	196	0	Normal	150	N	0.0	Up	0
219	29	M	ATA	140	263	0	Normal	170	N	0.0	Up	0
220	46	M	ASY	130	222	0	Normal	112	N	0.0	Flat	1
221	51	F	ASY	160	303	0	Normal	150	Y	1.0	Flat	1
222	48	F	NAP	120	195	0	Normal	125	N	0.0	Up	0
223	33	M	NAP	120	298	0	Normal	185	N	0.0	Up	0
224	55	M	ATA	120	256	1	Normal	137	N	0.0	Up	0
225	50	M	ASY	145	264	0	Normal	150	N	0.0	Flat	1
226	53	M	NAP	120	195	0	Normal	140	N	0.0	Up	0
227	38	M	ASY	92	117	0	Normal	134	Y	2.5	Flat	1
228	41	M	ATA	120	295	0	Normal	170	N	0.0	Up	0
229	37	F	ASY	130	173	0	ST	184	N	0.0	Up	0
230	37	M	ASY	130	315	0	Normal	158	N	0.0	Up	0
231	40	M	NAP	130	281	0	Normal	167	N	0.0	Up	0
232	38	F	ATA	120	275	0	Normal	129	N	0.0	Up	0
233	41	M	ASY	112	250	0	Normal	142	N	0.0	Up	0
234	54	F	ATA	140	309	0	ST	140	N	0.0	Up	0
235	39	M	ATA	120	200	0	Normal	160	Y	1.0	Flat	0
236	41	M	ASY	120	336	0	Normal	118	Y	3.0	Flat	1
237	55	M	TA	140	295	0	Normal	136	N	0.0	Flat	1
238	48	M	ASY	160	355	0	Normal	99	Y	2.0	Flat	1
239	48	M	ASY	160	193	0	Normal	102	Y	3.0	Flat	1
240	55	M	ATA	145	326	0	Normal	155	N	0.0	Up	0
241	54	M	ASY	200	198	0	Normal	142	Y	2.0	Flat	1
242	55	M	ATA	160	292	1	Normal	143	Y	2.0	Flat	1
243	43	F	ATA	120	266	0	Normal	118	N	0.0	Up	0
244	48	M	ASY	160	268	0	Normal	103	Y	1.0	Flat	1
245	54	M	TA	120	171	0	Normal	137	N	2.0	Up	0
246	54	M	NAP	120	237	0	Normal	150	Y	1.5	Flat	1
247	48	M	ASY	122	275	1	ST	150	Y	2.0	Down	1
248	45	M	ASY	130	219	0	ST	130	Y	1.0	Flat	1
249	49	M	ASY	130	341	0	Normal	120	Y	1.0	Flat	1
250	44	M	ASY	135	491	0	Normal	135	N	0.0	Flat	1
251	48	M	ASY	120	260	0	Normal	115	N	2.0	Flat	1
252	61	M	ASY	125	292	0	ST	115	Y	0.0	Up	0
253	62	M	ATA	140	271	0	Normal	152	N	1.0	Up	0
254	55	M	ASY	145	248	0	Normal	96	Y	2.0	Flat	1
255	53	F	NAP	120	274	0	Normal	130	N	0.0	Up	0
256	55	F	ATA	130	394	0	LVH	150	N	0.0	Up	0
257	36	M	NAP	150	160	0	Normal	172	N	0.0	Up	0
258	51	F	NAP	150	200	0	Normal	120	N	0.5	Up	0
259	55	F	ATA	122	320	0	Normal	155	N	0.0	Up	0
260	46	M	ATA	140	275	0	Normal	165	Y	0.0	Up	0
261	54	F	ATA	120	221	0	Normal	138	N	1.0	Up	0
262	46	M	ASY	120	231	0	Normal	115	Y	0.0	Flat	1
263	59	M	ASY	130	126	0	Normal	125	N	0.0	Flat	1
264	47	M	NAP	140	193	0	Normal	145	Y	1.0	Flat	1
265	54	M	ATA	160	305	0	Normal	175	N	0.0	Up	0
266	52	M	ASY	130	298	0	Normal	110	Y	1.0	Flat	1
267	34	M	ATA	98	220	0	Normal	150	N	0.0	Up	0
268	54	M	ASY	130	242	0	Normal	91	Y	1.0	Flat	1
269	47	F	NAP	130	235	0	Normal	145	N	2.0	Flat	0
270	45	M	ASY	120	225	0	Normal	140	N	0.0	Up	0
271	32	F	ATA	105	198	0	Normal	165	N	0.0	Up	0
272	55	M	ASY	140	201	0	Normal	130	Y	3.0	Flat	1
273	55	M	NAP	120	220	0	LVH	134	N	0.0	Up	0
274	45	F	ATA	180	295	0	Normal	180	N	0.0	Up	0
275	59	M	NAP	180	213	0	Normal	100	N	0.0	Up	0
276	51	M	NAP	135	160	0	Normal	150	N	2.0	Flat	1
277	52	M	ASY	170	223	0	Normal	126	Y	1.5	Flat	1
278	57	F	ASY	180	347	0	ST	126	Y	0.8	Flat	0
279	54	F	ATA	130	253	0	ST	155	N	0.0	Up	0
280	60	M	NAP	120	246	0	LVH	135	N	0.0	Up	0
281	49	M	ASY	150	222	0	Normal	122	N	2.0	Flat	1
282	51	F	NAP	130	220	0	Normal	160	Y	2.0	Up	0
283	55	F	ATA	110	344	0	ST	160	N	0.0	Up	0
284	42	M	ASY	140	358	0	Normal	170	N	0.0	Up	0
285	51	F	NAP	110	190	0	Normal	120	N	0.0	Up	0
286	59	M	ASY	140	169	0	Normal	140	N	0.0	Up	0
287	53	M	ATA	120	181	0	Normal	132	N	0.0	Up	0
288	48	F	ATA	133	308	0	ST	156	N	2.0	Up	0
289	36	M	ATA	120	166	0	Normal	180	N	0.0	Up	0
290	48	M	NAP	110	211	0	Normal	138	N	0.0	Up	0
291	47	F	ATA	140	257	0	Normal	135	N	1.0	Up	0
292	53	M	ASY	130	182	0	Normal	148	N	0.0	Up	0
293	65	M	ASY	115	0	0	Normal	93	Y	0.0	Flat	1
294	32	M	TA	95	0	1	Normal	127	N	0.7	Up	1
295	61	M	ASY	105	0	1	Normal	110	Y	1.5	Up	1
296	50	M	ASY	145	0	1	Normal	139	Y	0.7	Flat	1
297	57	M	ASY	110	0	1	ST	131	Y	1.4	Up	1
298	51	M	ASY	110	0	1	Normal	92	N	0.0	Flat	1
299	47	M	ASY	110	0	1	ST	149	N	2.1	Up	1
300	60	M	ASY	160	0	1	Normal	149	N	0.4	Flat	1
301	55	M	ATA	140	0	0	ST	150	N	0.2	Up	0
302	53	M	ASY	125	0	1	Normal	120	N	1.5	Up	1
303	62	F	ASY	120	0	1	ST	123	Y	1.7	Down	1
304	51	M	ASY	95	0	1	Normal	126	N	2.2	Flat	1
305	51	F	ASY	120	0	1	Normal	127	Y	1.5	Up	1
306	55	M	ASY	115	0	1	Normal	155	N	0.1	Flat	1
307	53	M	ATA	130	0	0	ST	120	N	0.7	Down	0
308	58	M	ASY	115	0	1	Normal	138	N	0.5	Up	1
309	57	M	ASY	95	0	1	Normal	182	N	0.7	Down	1
310	65	M	ASY	155	0	0	Normal	154	N	1.0	Up	0
311	60	M	ASY	125	0	1	Normal	110	N	0.1	Up	1
312	41	M	ASY	125	0	1	Normal	176	N	1.6	Up	1
313	34	M	ASY	115	0	1	Normal	154	N	0.2	Up	1
314	53	M	ASY	80	0	0	Normal	141	Y	2.0	Down	0
315	74	M	ATA	145	0	1	ST	123	N	1.3	Up	1
316	57	M	NAP	105	0	1	Normal	148	N	0.3	Flat	1
317	56	M	ASY	140	0	1	Normal	121	Y	1.8	Up	1
318	61	M	ASY	130	0	1	Normal	77	N	2.5	Flat	1
319	68	M	ASY	145	0	1	Normal	136	N	1.8	Up	1
320	59	M	NAP	125	0	1	Normal	175	N	2.6	Flat	1
321	63	M	ASY	100	0	1	Normal	109	N	-0.9	Flat	1
322	38	F	ASY	105	0	1	Normal	166	N	2.8	Up	1
323	62	M	ASY	115	0	1	Normal	128	Y	2.5	Down	1
324	46	M	ASY	100	0	1	ST	133	N	-2.6	Flat	1
325	42	M	ASY	105	0	1	Normal	128	Y	-1.5	Down	1
326	45	M	NAP	110	0	0	Normal	138	N	-0.1	Up	0
327	59	M	ASY	125	0	1	Normal	119	Y	0.9	Up	1
328	52	M	ASY	95	0	1	Normal	82	Y	0.8	Flat	1
329	60	M	ASY	130	0	1	ST	130	Y	1.1	Down	1
330	60	M	NAP	115	0	1	Normal	143	N	2.4	Up	1
331	56	M	ASY	115	0	1	ST	82	N	-1.0	Up	1
332	38	M	NAP	100	0	0	Normal	179	N	-1.1	Up	0
333	40	M	ASY	95	0	1	ST	144	N	0.0	Up	1
334	51	M	ASY	130	0	1	Normal	170	N	-0.7	Up	1
335	62	M	TA	120	0	1	LVH	134	N	-0.8	Flat	1
336	72	M	NAP	160	0	0	LVH	114	N	1.6	Flat	0
337	63	M	ASY	150	0	1	ST	154	N	3.7	Up	1
338	63	M	ASY	140	0	1	LVH	149	N	2.0	Up	1
339	64	F	ASY	95	0	1	Normal	145	N	1.1	Down	1
340	43	M	ASY	100	0	1	Normal	122	N	1.5	Down	1
341	64	M	ASY	110	0	1	Normal	114	Y	1.3	Down	1
342	61	M	ASY	110	0	1	Normal	113	N	1.4	Flat	1
343	52	M	ASY	130	0	1	Normal	120	N	0.0	Flat	1
344	51	M	ASY	120	0	1	Normal	104	N	0.0	Flat	1
345	69	M	ASY	135	0	0	Normal	130	N	0.0	Flat	1
346	59	M	ASY	120	0	0	Normal	115	N	0.0	Flat	1
347	48	M	ASY	115	0	1	Normal	128	N	0.0	Flat	1
348	69	M	ASY	137	0	0	ST	104	Y	1.6	Flat	1
349	36	M	ASY	110	0	1	Normal	125	Y	1.0	Flat	1
350	53	M	ASY	120	0	1	Normal	120	N	0.0	Flat	1
351	43	M	ASY	140	0	0	ST	140	Y	0.5	Up	1
352	56	M	ASY	120	0	0	ST	100	Y	-1.0	Down	1
353	58	M	ASY	130	0	0	ST	100	Y	1.0	Flat	1
354	55	M	ASY	120	0	0	ST	92	N	0.3	Up	1
355	67	M	TA	145	0	0	LVH	125	N	0.0	Flat	1
356	46	M	ASY	115	0	0	Normal	113	Y	1.5	Flat	1
357	53	M	ATA	120	0	0	Normal	95	N	0.0	Flat	1
358	38	M	NAP	115	0	0	Normal	128	Y	0.0	Flat	1
359	53	M	NAP	105	0	0	Normal	115	N	0.0	Flat	1
360	62	M	NAP	160	0	0	Normal	72	Y	0.0	Flat	1
361	47	M	ASY	160	0	0	Normal	124	Y	0.0	Flat	1
362	56	M	NAP	155	0	0	ST	99	N	0.0	Flat	1
363	56	M	ASY	120	0	0	ST	148	N	0.0	Flat	1
364	56	M	NAP	120	0	0	Normal	97	N	0.0	Flat	0
365	64	F	ASY	200	0	0	Normal	140	Y	1.0	Flat	1
366	61	M	ASY	150	0	0	Normal	117	Y	2.0	Flat	1
367	68	M	ASY	135	0	0	ST	120	Y	0.0	Up	1
368	57	M	ASY	140	0	0	Normal	120	Y	2.0	Flat	1
369	63	M	ASY	150	0	0	Normal	86	Y	2.0	Flat	1
370	60	M	ASY	135	0	0	Normal	63	Y	0.5	Up	1
371	66	M	ASY	150	0	0	Normal	108	Y	2.0	Flat	1
372	63	M	ASY	185	0	0	Normal	98	Y	0.0	Up	1
373	59	M	ASY	135	0	0	Normal	115	Y	1.0	Flat	1
374	61	M	ASY	125	0	0	Normal	105	Y	0.0	Down	1
375	73	F	NAP	160	0	0	ST	121	N	0.0	Up	1
376	47	M	NAP	155	0	0	Normal	118	Y	1.0	Flat	1
377	65	M	ASY	160	0	1	ST	122	N	1.2	Flat	1
378	70	M	ASY	140	0	1	Normal	157	Y	2.0	Flat	1
379	50	M	ASY	120	0	0	ST	156	Y	0.0	Up	1
380	60	M	ASY	160	0	0	ST	99	Y	0.5	Flat	1
381	50	M	ASY	115	0	0	Normal	120	Y	0.5	Flat	1
382	43	M	ASY	115	0	0	Normal	145	Y	2.0	Flat	1
383	38	F	ASY	110	0	0	Normal	156	N	0.0	Flat	1
384	54	M	ASY	120	0	0	Normal	155	N	0.0	Flat	1
385	61	M	ASY	150	0	0	Normal	105	Y	0.0	Flat	1
386	42	M	ASY	145	0	0	Normal	99	Y	0.0	Flat	1
387	53	M	ASY	130	0	0	LVH	135	Y	1.0	Flat	1
388	55	M	ASY	140	0	0	Normal	83	N	0.0	Flat	1
389	61	M	ASY	160	0	1	ST	145	N	1.0	Flat	1
390	51	M	ASY	140	0	0	Normal	60	N	0.0	Flat	1
391	70	M	ASY	115	0	0	ST	92	Y	0.0	Flat	1
392	61	M	ASY	130	0	0	LVH	115	N	0.0	Flat	1
393	38	M	ASY	150	0	1	Normal	120	Y	0.7	Flat	1
394	57	M	ASY	160	0	1	Normal	98	Y	2.0	Flat	1
395	38	M	ASY	135	0	1	Normal	150	N	0.0	Flat	1
396	62	F	TA	140	0	1	Normal	143	N	0.0	Flat	1
397	58	M	ASY	170	0	1	ST	105	Y	0.0	Flat	1
398	52	M	ASY	165	0	1	Normal	122	Y	1.0	Up	1
399	61	M	NAP	200	0	1	ST	70	N	0.0	Flat	1
400	50	F	ASY	160	0	1	Normal	110	N	0.0	Flat	1
401	51	M	ASY	130	0	1	ST	163	N	0.0	Flat	1
402	65	M	ASY	145	0	1	ST	67	N	0.7	Flat	1
403	52	M	ASY	135	0	1	Normal	128	Y	2.0	Flat	1
404	47	M	NAP	110	0	1	Normal	120	Y	0.0	Flat	1
405	35	M	ASY	120	0	1	Normal	130	Y	1.2	Flat	1
406	57	M	ASY	140	0	1	Normal	100	Y	0.0	Flat	1
407	62	M	ASY	115	0	1	Normal	72	Y	-0.5	Flat	1
408	59	M	ASY	110	0	1	Normal	94	N	0.0	Flat	1
409	53	M	NAP	160	0	1	LVH	122	Y	0.0	Flat	1
410	62	M	ASY	150	0	1	ST	78	N	2.0	Flat	1
411	54	M	ASY	180	0	1	Normal	150	N	1.5	Flat	1
412	56	M	ASY	125	0	1	Normal	103	Y	1.0	Flat	1
413	56	M	NAP	125	0	1	Normal	98	N	-2.0	Flat	1
414	54	M	ASY	130	0	1	Normal	110	Y	3.0	Flat	1
415	66	F	ASY	155	0	1	Normal	90	N	0.0	Flat	1
416	63	M	ASY	140	260	0	ST	112	Y	3.0	Flat	1
417	44	M	ASY	130	209	0	ST	127	N	0.0	Up	0
418	60	M	ASY	132	218	0	ST	140	Y	1.5	Down	1
419	55	M	ASY	142	228	0	ST	149	Y	2.5	Up	1
420	66	M	NAP	110	213	1	LVH	99	Y	1.3	Flat	0
421	66	M	NAP	120	0	0	ST	120	N	-0.5	Up	0
422	65	M	ASY	150	236	1	ST	105	Y	0.0	Flat	1
423	60	M	NAP	180	0	0	ST	140	Y	1.5	Flat	0
424	60	M	NAP	120	0	1	Normal	141	Y	2.0	Up	1
425	60	M	ATA	160	267	1	ST	157	N	0.5	Flat	1
426	56	M	ATA	126	166	0	ST	140	N	0.0	Up	0
427	59	M	ASY	140	0	0	ST	117	Y	1.0	Flat	1
428	62	M	ASY	110	0	0	Normal	120	Y	0.5	Flat	1
429	63	M	NAP	133	0	0	LVH	120	Y	1.0	Flat	1
430	57	M	ASY	128	0	1	ST	148	Y	1.0	Flat	1
431	62	M	ASY	120	220	0	ST	86	N	0.0	Up	0
432	63	M	ASY	170	177	0	Normal	84	Y	2.5	Down	1
433	46	M	ASY	110	236	0	Normal	125	Y	2.0	Flat	1
434	63	M	ASY	126	0	0	ST	120	N	1.5	Down	0
435	60	M	ASY	152	0	0	ST	118	Y	0.0	Up	0
436	58	M	ASY	116	0	0	Normal	124	N	1.0	Up	1
437	64	M	ASY	120	0	1	ST	106	N	2.0	Flat	1
438	63	M	NAP	130	0	0	ST	111	Y	0.0	Flat	1
439	74	M	NAP	138	0	0	Normal	116	N	0.2	Up	0
440	52	M	NAP	128	0	0	ST	180	N	3.0	Up	1
441	69	M	ASY	130	0	1	ST	129	N	1.0	Flat	1
442	51	M	ASY	128	0	1	ST	125	Y	1.2	Flat	1
443	60	M	ASY	130	186	1	ST	140	Y	0.5	Flat	1
444	56	M	ASY	120	100	0	Normal	120	Y	1.5	Flat	1
445	55	M	NAP	136	228	0	ST	124	Y	1.6	Flat	1
446	54	M	ASY	130	0	0	ST	117	Y	1.4	Flat	1
447	77	M	ASY	124	171	0	ST	110	Y	2.0	Up	1
448	63	M	ASY	160	230	1	Normal	105	Y	1.0	Flat	1
449	55	M	NAP	0	0	0	Normal	155	N	1.5	Flat	1
450	52	M	NAP	122	0	0	Normal	110	Y	2.0	Down	1
451	64	M	ASY	144	0	0	ST	122	Y	1.0	Flat	1
452	60	M	ASY	140	281	0	ST	118	Y	1.5	Flat	1
453	60	M	ASY	120	0	0	Normal	133	Y	2.0	Up	0
454	58	M	ASY	136	203	1	Normal	123	Y	1.2	Flat	1
455	59	M	ASY	154	0	0	ST	131	Y	1.5	Up	0
456	61	M	NAP	120	0	0	Normal	80	Y	0.0	Flat	1
457	40	M	ASY	125	0	1	Normal	165	N	0.0	Flat	1
458	61	M	ASY	134	0	1	ST	86	N	1.5	Flat	1
459	41	M	ASY	104	0	0	ST	111	N	0.0	Up	0
460	57	M	ASY	139	277	1	ST	118	Y	1.9	Flat	1
461	63	M	ASY	136	0	0	Normal	84	Y	0.0	Flat	1
462	59	M	ASY	122	233	0	Normal	117	Y	1.3	Down	1
463	51	M	ASY	128	0	0	Normal	107	N	0.0	Up	0
464	59	M	NAP	131	0	0	Normal	128	Y	2.0	Down	1
465	42	M	NAP	134	240	0	Normal	160	N	0.0	Up	0
466	55	M	NAP	120	0	0	ST	125	Y	2.5	Flat	1
467	63	F	ATA	132	0	0	Normal	130	N	0.1	Up	0
468	62	M	ASY	152	153	0	ST	97	Y	1.6	Up	1
469	56	M	ATA	124	224	1	Normal	161	N	2.0	Flat	0
470	53	M	ASY	126	0	0	Normal	106	N	0.0	Flat	1
471	68	M	ASY	138	0	0	Normal	130	Y	3.0	Flat	1
472	53	M	ASY	154	0	1	ST	140	Y	1.5	Flat	1
473	60	M	NAP	141	316	1	ST	122	Y	1.7	Flat	1
474	62	M	ATA	131	0	0	Normal	130	N	0.1	Up	0
475	59	M	ASY	178	0	1	LVH	120	Y	0.0	Flat	1
476	51	M	ASY	132	218	1	LVH	139	N	0.1	Up	0
477	61	M	ASY	110	0	1	Normal	108	Y	2.0	Down	1
478	57	M	ASY	130	311	1	ST	148	Y	2.0	Flat	1
479	56	M	NAP	170	0	0	LVH	123	Y	2.5	Flat	1
480	58	M	ATA	126	0	1	Normal	110	Y	2.0	Flat	1
481	69	M	NAP	140	0	1	ST	118	N	2.5	Down	1
482	67	M	TA	142	270	1	Normal	125	N	2.5	Up	1
483	58	M	ASY	120	0	0	LVH	106	Y	1.5	Down	1
484	65	M	ASY	134	0	0	Normal	112	Y	1.1	Flat	1
485	63	M	ATA	139	217	1	ST	128	Y	1.2	Flat	1
486	55	M	ATA	110	214	1	ST	180	N	0.4	Up	0
487	57	M	ASY	140	214	0	ST	144	Y	2.0	Flat	1
488	65	M	TA	140	252	0	Normal	135	N	0.3	Up	0
489	54	M	ASY	136	220	0	Normal	140	Y	3.0	Flat	1
490	72	M	NAP	120	214	0	Normal	102	Y	1.0	Flat	1
491	75	M	ASY	170	203	1	ST	108	N	0.0	Flat	1
492	49	M	TA	130	0	0	ST	145	N	3.0	Flat	1
493	51	M	NAP	137	339	0	Normal	127	Y	1.7	Flat	1
494	60	M	ASY	142	216	0	Normal	110	Y	2.5	Flat	1
495	64	F	ASY	142	276	0	Normal	140	Y	1.0	Flat	1
496	58	M	ASY	132	458	1	Normal	69	N	1.0	Down	0
497	61	M	ASY	146	241	0	Normal	148	Y	3.0	Down	1
498	67	M	ASY	160	384	1	ST	130	Y	0.0	Flat	1
499	62	M	ASY	135	297	0	Normal	130	Y	1.0	Flat	1
500	65	M	ASY	136	248	0	Normal	140	Y	4.0	Down	1
501	63	M	ASY	130	308	0	Normal	138	Y	2.0	Flat	1
502	69	M	ASY	140	208	0	ST	140	Y	2.0	Flat	1
503	51	M	ASY	132	227	1	ST	138	N	0.2	Up	0
504	62	M	ASY	158	210	1	Normal	112	Y	3.0	Down	1
505	55	M	NAP	136	245	1	ST	131	Y	1.2	Flat	1
506	75	M	ASY	136	225	0	Normal	112	Y	3.0	Flat	1
507	40	M	NAP	106	240	0	Normal	80	Y	0.0	Up	0
508	67	M	ASY	120	0	1	Normal	150	N	1.5	Down	1
509	58	M	ASY	110	198	0	Normal	110	N	0.0	Flat	1
510	60	M	ASY	136	195	0	Normal	126	N	0.3	Up	0
511	63	M	ASY	160	267	1	ST	88	Y	2.0	Flat	1
512	35	M	NAP	123	161	0	ST	153	N	-0.1	Up	0
513	62	M	TA	112	258	0	ST	150	Y	1.3	Flat	1
514	43	M	ASY	122	0	0	Normal	120	N	0.5	Up	1
515	63	M	NAP	130	0	1	ST	160	N	3.0	Flat	0
516	68	M	NAP	150	195	1	Normal	132	N	0.0	Flat	1
517	65	M	ASY	150	235	0	Normal	120	Y	1.5	Flat	1
518	48	M	NAP	102	0	1	ST	110	Y	1.0	Down	1
519	63	M	ASY	96	305	0	ST	121	Y	1.0	Up	1
520	64	M	ASY	130	223	0	ST	128	N	0.5	Flat	0
521	61	M	ASY	120	282	0	ST	135	Y	4.0	Down	1
522	50	M	ASY	144	349	0	LVH	120	Y	1.0	Up	1
523	59	M	ASY	124	160	0	Normal	117	Y	1.0	Flat	1
524	55	M	ASY	150	160	0	ST	150	N	0.0	Up	0
525	45	M	NAP	130	236	0	Normal	144	N	0.1	Up	0
526	65	M	ASY	144	312	0	LVH	113	Y	1.7	Flat	1
527	61	M	ATA	139	283	0	Normal	135	N	0.3	Up	0
528	49	M	NAP	131	142	0	Normal	127	Y	1.5	Flat	1
529	72	M	ASY	143	211	0	Normal	109	Y	1.4	Flat	1
530	50	M	ASY	133	218	0	Normal	128	Y	1.1	Flat	1
531	64	M	ASY	143	306	1	ST	115	Y	1.8	Flat	1
532	55	M	ASY	116	186	1	ST	102	N	0.0	Flat	1
533	63	M	ASY	110	252	0	ST	140	Y	2.0	Flat	1
534	59	M	ASY	125	222	0	Normal	135	Y	2.5	Down	1
535	56	M	ASY	130	0	0	LVH	122	Y	1.0	Flat	1
536	62	M	NAP	133	0	1	ST	119	Y	1.2	Flat	1
537	74	M	ASY	150	258	1	ST	130	Y	4.0	Down	1
538	54	M	ASY	130	202	1	Normal	112	Y	2.0	Flat	1
539	57	M	ASY	110	197	0	LVH	100	N	0.0	Up	0
540	62	M	NAP	138	204	0	ST	122	Y	1.2	Flat	1
541	76	M	NAP	104	113	0	LVH	120	N	3.5	Down	1
542	54	F	ASY	138	274	0	Normal	105	Y	1.5	Flat	1
543	70	M	ASY	170	192	0	ST	129	Y	3.0	Down	1
544	61	F	ATA	140	298	1	Normal	120	Y	0.0	Up	0
545	48	M	ASY	132	272	0	ST	139	N	0.2	Up	0
546	48	M	NAP	132	220	1	ST	162	N	0.0	Flat	1
547	61	M	TA	142	200	1	ST	100	N	1.5	Down	1
548	66	M	ASY	112	261	0	Normal	140	N	1.5	Up	1
549	68	M	TA	139	181	1	ST	135	N	0.2	Up	0
550	55	M	ASY	172	260	0	Normal	73	N	2.0	Flat	1
551	62	M	NAP	120	220	0	LVH	86	N	0.0	Up	0
552	71	M	NAP	144	221	0	Normal	108	Y	1.8	Flat	1
553	74	M	TA	145	216	1	Normal	116	Y	1.8	Flat	1
554	53	M	NAP	155	175	1	ST	160	N	0.3	Up	0
555	58	M	NAP	150	219	0	ST	118	Y	0.0	Flat	1
556	75	M	ASY	160	310	1	Normal	112	Y	2.0	Down	0
557	56	M	NAP	137	208	1	ST	122	Y	1.8	Flat	1
558	58	M	NAP	137	232	0	ST	124	Y	1.4	Flat	1
559	64	M	ASY	134	273	0	Normal	102	Y	4.0	Down	1
560	54	M	NAP	133	203	0	ST	137	N	0.2	Up	0
561	54	M	ATA	132	182	0	ST	141	N	0.1	Up	0
562	59	M	ASY	140	274	0	Normal	154	Y	2.0	Flat	0
563	55	M	ASY	135	204	1	ST	126	Y	1.1	Flat	1
564	57	M	ASY	144	270	1	ST	160	Y	2.0	Flat	1
565	61	M	ASY	141	292	0	ST	115	Y	1.7	Flat	1
566	41	M	ASY	150	171	0	Normal	128	Y	1.5	Flat	0
567	71	M	ASY	130	221	0	ST	115	Y	0.0	Flat	1
568	38	M	ASY	110	289	0	Normal	105	Y	1.5	Down	1
569	55	M	ASY	158	217	0	Normal	110	Y	2.5	Flat	1
570	56	M	ASY	128	223	0	ST	119	Y	2.0	Down	1
571	69	M	ASY	140	110	1	Normal	109	Y	1.5	Flat	1
572	64	M	ASY	150	193	0	ST	135	Y	0.5	Flat	1
573	72	M	ASY	160	123	1	LVH	130	N	1.5	Flat	1
574	69	M	ASY	142	210	1	ST	112	Y	1.5	Flat	1
575	56	M	ASY	137	282	1	Normal	126	Y	1.2	Flat	1
576	62	M	ASY	139	170	0	ST	120	Y	3.0	Flat	1
577	67	M	ASY	146	369	0	Normal	110	Y	1.9	Flat	1
578	57	M	ASY	156	173	0	LVH	119	Y	3.0	Down	1
579	69	M	ASY	145	289	1	ST	110	Y	1.8	Flat	1
580	51	M	ASY	131	152	1	LVH	130	Y	1.0	Flat	1
581	48	M	ASY	140	208	0	Normal	159	Y	1.5	Up	1
582	69	M	ASY	122	216	1	LVH	84	Y	0.0	Flat	1
583	69	M	NAP	142	271	0	LVH	126	N	0.3	Up	0
584	64	M	ASY	141	244	1	ST	116	Y	1.5	Flat	1
585	57	M	ATA	180	285	1	ST	120	N	0.8	Flat	1
586	53	M	ASY	124	243	0	Normal	122	Y	2.0	Flat	1
587	37	M	NAP	118	240	0	LVH	165	N	1.0	Flat	0
588	67	M	ASY	140	219	0	ST	122	Y	2.0	Flat	1
589	74	M	NAP	140	237	1	Normal	94	N	0.0	Flat	1
590	63	M	ATA	136	165	0	ST	133	N	0.2	Up	0
591	58	M	ASY	100	213	0	ST	110	N	0.0	Up	0
592	61	M	ASY	190	287	1	LVH	150	Y	2.0	Down	1
593	64	M	ASY	130	258	1	LVH	130	N	0.0	Flat	1
594	58	M	ASY	160	256	1	LVH	113	Y	1.0	Up	1
595	60	M	ASY	130	186	1	LVH	140	Y	0.5	Flat	1
596	57	M	ASY	122	264	0	LVH	100	N	0.0	Flat	1
597	55	M	NAP	133	185	0	ST	136	N	0.2	Up	0
598	55	M	ASY	120	226	0	LVH	127	Y	1.7	Down	1
599	56	M	ASY	130	203	1	Normal	98	N	1.5	Flat	1
600	57	M	ASY	130	207	0	ST	96	Y	1.0	Flat	0
601	61	M	NAP	140	284	0	Normal	123	Y	1.3	Flat	1
602	61	M	NAP	120	337	0	Normal	98	Y	0.0	Flat	1
603	74	M	ASY	155	310	0	Normal	112	Y	1.5	Down	1
604	68	M	NAP	134	254	1	Normal	151	Y	0.0	Up	0
605	51	F	ASY	114	258	1	LVH	96	N	1.0	Up	0
606	62	M	ASY	160	254	1	ST	108	Y	3.0	Flat	1
607	53	M	ASY	144	300	1	ST	128	Y	1.5	Flat	1
608	62	M	ASY	158	170	0	ST	138	Y	0.0	Flat	1
609	46	M	ASY	134	310	0	Normal	126	N	0.0	Flat	1
610	54	F	ASY	127	333	1	ST	154	N	0.0	Flat	1
611	62	M	TA	135	139	0	ST	137	N	0.2	Up	0
612	55	M	ASY	122	223	1	ST	100	N	0.0	Flat	1
613	58	M	ASY	140	385	1	LVH	135	N	0.3	Up	0
614	62	M	ATA	120	254	0	LVH	93	Y	0.0	Flat	1
615	70	M	ASY	130	322	0	LVH	109	N	2.4	Flat	1
616	67	F	NAP	115	564	0	LVH	160	N	1.6	Flat	0
617	57	M	ATA	124	261	0	Normal	141	N	0.3	Up	1
618	64	M	ASY	128	263	0	Normal	105	Y	0.2	Flat	0
619	74	F	ATA	120	269	0	LVH	121	Y	0.2	Up	0
620	65	M	ASY	120	177	0	Normal	140	N	0.4	Up	0
621	56	M	NAP	130	256	1	LVH	142	Y	0.6	Flat	1
622	59	M	ASY	110	239	0	LVH	142	Y	1.2	Flat	1
623	60	M	ASY	140	293	0	LVH	170	N	1.2	Flat	1
624	63	F	ASY	150	407	0	LVH	154	N	4.0	Flat	1
625	59	M	ASY	135	234	0	Normal	161	N	0.5	Flat	0
626	53	M	ASY	142	226	0	LVH	111	Y	0.0	Up	0
627	44	M	NAP	140	235	0	LVH	180	N	0.0	Up	0
628	61	M	TA	134	234	0	Normal	145	N	2.6	Flat	1
629	57	F	ASY	128	303	0	LVH	159	N	0.0	Up	0
630	71	F	ASY	112	149	0	Normal	125	N	1.6	Flat	0
631	46	M	ASY	140	311	0	Normal	120	Y	1.8	Flat	1
632	53	M	ASY	140	203	1	LVH	155	Y	3.1	Down	1
633	64	M	TA	110	211	0	LVH	144	Y	1.8	Flat	0
634	40	M	TA	140	199	0	Normal	178	Y	1.4	Up	0
635	67	M	ASY	120	229	0	LVH	129	Y	2.6	Flat	1
636	48	M	ATA	130	245	0	LVH	180	N	0.2	Flat	0
637	43	M	ASY	115	303	0	Normal	181	N	1.2	Flat	0
638	47	M	ASY	112	204	0	Normal	143	N	0.1	Up	0
639	54	F	ATA	132	288	1	LVH	159	Y	0.0	Up	0
640	48	F	NAP	130	275	0	Normal	139	N	0.2	Up	0
641	46	F	ASY	138	243	0	LVH	152	Y	0.0	Flat	0
642	51	F	NAP	120	295	0	LVH	157	N	0.6	Up	0
643	58	M	NAP	112	230	0	LVH	165	N	2.5	Flat	1
644	71	F	NAP	110	265	1	LVH	130	N	0.0	Up	0
645	57	M	NAP	128	229	0	LVH	150	N	0.4	Flat	1
646	66	M	ASY	160	228	0	LVH	138	N	2.3	Up	0
647	37	F	NAP	120	215	0	Normal	170	N	0.0	Up	0
648	59	M	ASY	170	326	0	LVH	140	Y	3.4	Down	1
649	50	M	ASY	144	200	0	LVH	126	Y	0.9	Flat	1
650	48	M	ASY	130	256	1	LVH	150	Y	0.0	Up	1
651	61	M	ASY	140	207	0	LVH	138	Y	1.9	Up	1
652	59	M	TA	160	273	0	LVH	125	N	0.0	Up	1
653	42	M	NAP	130	180	0	Normal	150	N	0.0	Up	0
654	48	M	ASY	122	222	0	LVH	186	N	0.0	Up	0
655	40	M	ASY	152	223	0	Normal	181	N	0.0	Up	1
656	62	F	ASY	124	209	0	Normal	163	N	0.0	Up	0
657	44	M	NAP	130	233	0	Normal	179	Y	0.4	Up	0
658	46	M	ATA	101	197	1	Normal	156	N	0.0	Up	0
659	59	M	NAP	126	218	1	Normal	134	N	2.2	Flat	1
660	58	M	NAP	140	211	1	LVH	165	N	0.0	Up	0
661	49	M	NAP	118	149	0	LVH	126	N	0.8	Up	1
662	44	M	ASY	110	197	0	LVH	177	N	0.0	Up	1
663	66	M	ATA	160	246	0	Normal	120	Y	0.0	Flat	1
664	65	F	ASY	150	225	0	LVH	114	N	1.0	Flat	1
665	42	M	ASY	136	315	0	Normal	125	Y	1.8	Flat	1
666	52	M	ATA	128	205	1	Normal	184	N	0.0	Up	0
667	65	F	NAP	140	417	1	LVH	157	N	0.8	Up	0
668	63	F	ATA	140	195	0	Normal	179	N	0.0	Up	0
669	45	F	ATA	130	234	0	LVH	175	N	0.6	Flat	0
670	41	F	ATA	105	198	0	Normal	168	N	0.0	Up	0
671	61	M	ASY	138	166	0	LVH	125	Y	3.6	Flat	1
672	60	F	NAP	120	178	1	Normal	96	N	0.0	Up	0
673	59	F	ASY	174	249	0	Normal	143	Y	0.0	Flat	1
674	62	M	ATA	120	281	0	LVH	103	N	1.4	Flat	1
675	57	M	NAP	150	126	1	Normal	173	N	0.2	Up	0
676	51	F	ASY	130	305	0	Normal	142	Y	1.2	Flat	1
677	44	M	NAP	120	226	0	Normal	169	N	0.0	Up	0
678	60	F	TA	150	240	0	Normal	171	N	0.9	Up	0
679	63	M	TA	145	233	1	LVH	150	N	2.3	Down	0
680	57	M	ASY	150	276	0	LVH	112	Y	0.6	Flat	1
681	51	M	ASY	140	261	0	LVH	186	Y	0.0	Up	0
682	58	F	ATA	136	319	1	LVH	152	N	0.0	Up	1
683	44	F	NAP	118	242	0	Normal	149	N	0.3	Flat	0
684	47	M	NAP	108	243	0	Normal	152	N	0.0	Up	1
685	61	M	ASY	120	260	0	Normal	140	Y	3.6	Flat	1
686	57	F	ASY	120	354	0	Normal	163	Y	0.6	Up	0
687	70	M	ATA	156	245	0	LVH	143	N	0.0	Up	0
688	76	F	NAP	140	197	0	ST	116	N	1.1	Flat	0
689	67	F	ASY	106	223	0	Normal	142	N	0.3	Up	0
690	45	M	ASY	142	309	0	LVH	147	Y	0.0	Flat	1
691	45	M	ASY	104	208	0	LVH	148	Y	3.0	Flat	0
692	39	F	NAP	94	199	0	Normal	179	N	0.0	Up	0
693	42	F	NAP	120	209	0	Normal	173	N	0.0	Flat	0
694	56	M	ATA	120	236	0	Normal	178	N	0.8	Up	0
695	58	M	ASY	146	218	0	Normal	105	N	2.0	Flat	1
696	35	M	ASY	120	198	0	Normal	130	Y	1.6	Flat	1
697	58	M	ASY	150	270	0	LVH	111	Y	0.8	Up	1
698	41	M	NAP	130	214	0	LVH	168	N	2.0	Flat	0
699	57	M	ASY	110	201	0	Normal	126	Y	1.5	Flat	0
700	42	M	TA	148	244	0	LVH	178	N	0.8	Up	0
701	62	M	ATA	128	208	1	LVH	140	N	0.0	Up	0
702	59	M	TA	178	270	0	LVH	145	N	4.2	Down	0
703	41	F	ATA	126	306	0	Normal	163	N	0.0	Up	0
704	50	M	ASY	150	243	0	LVH	128	N	2.6	Flat	1
705	59	M	ATA	140	221	0	Normal	164	Y	0.0	Up	0
706	61	F	ASY	130	330	0	LVH	169	N	0.0	Up	1
707	54	M	ASY	124	266	0	LVH	109	Y	2.2	Flat	1
708	54	M	ASY	110	206	0	LVH	108	Y	0.0	Flat	1
709	52	M	ASY	125	212	0	Normal	168	N	1.0	Up	1
710	47	M	ASY	110	275	0	LVH	118	Y	1.0	Flat	1
711	66	M	ASY	120	302	0	LVH	151	N	0.4	Flat	0
712	58	M	ASY	100	234	0	Normal	156	N	0.1	Up	1
713	64	F	NAP	140	313	0	Normal	133	N	0.2	Up	0
714	50	F	ATA	120	244	0	Normal	162	N	1.1	Up	0
715	44	F	NAP	108	141	0	Normal	175	N	0.6	Flat	0
716	67	M	ASY	120	237	0	Normal	71	N	1.0	Flat	1
717	49	F	ASY	130	269	0	Normal	163	N	0.0	Up	0
718	57	M	ASY	165	289	1	LVH	124	N	1.0	Flat	1
719	63	M	ASY	130	254	0	LVH	147	N	1.4	Flat	1
720	48	M	ASY	124	274	0	LVH	166	N	0.5	Flat	1
721	51	M	NAP	100	222	0	Normal	143	Y	1.2	Flat	0
722	60	F	ASY	150	258	0	LVH	157	N	2.6	Flat	1
723	59	M	ASY	140	177	0	Normal	162	Y	0.0	Up	1
724	45	F	ATA	112	160	0	Normal	138	N	0.0	Flat	0
725	55	F	ASY	180	327	0	ST	117	Y	3.4	Flat	1
726	41	M	ATA	110	235	0	Normal	153	N	0.0	Up	0
727	60	F	ASY	158	305	0	LVH	161	N	0.0	Up	1
728	54	F	NAP	135	304	1	Normal	170	N	0.0	Up	0
729	42	M	ATA	120	295	0	Normal	162	N	0.0	Up	0
730	49	F	ATA	134	271	0	Normal	162	N	0.0	Flat	0
731	46	M	ASY	120	249	0	LVH	144	N	0.8	Up	1
732	56	F	ASY	200	288	1	LVH	133	Y	4.0	Down	1
733	66	F	TA	150	226	0	Normal	114	N	2.6	Down	0
734	56	M	ASY	130	283	1	LVH	103	Y	1.6	Down	1
735	49	M	NAP	120	188	0	Normal	139	N	2.0	Flat	1
736	54	M	ASY	122	286	0	LVH	116	Y	3.2	Flat	1
737	57	M	ASY	152	274	0	Normal	88	Y	1.2	Flat	1
738	65	F	NAP	160	360	0	LVH	151	N	0.8	Up	0
739	54	M	NAP	125	273	0	LVH	152	N	0.5	Down	0
740	54	F	NAP	160	201	0	Normal	163	N	0.0	Up	0
741	62	M	ASY	120	267	0	Normal	99	Y	1.8	Flat	1
742	52	F	NAP	136	196	0	LVH	169	N	0.1	Flat	0
743	52	M	ATA	134	201	0	Normal	158	N	0.8	Up	0
744	60	M	ASY	117	230	1	Normal	160	Y	1.4	Up	1
745	63	F	ASY	108	269	0	Normal	169	Y	1.8	Flat	1
746	66	M	ASY	112	212	0	LVH	132	Y	0.1	Up	1
747	42	M	ASY	140	226	0	Normal	178	N	0.0	Up	0
748	64	M	ASY	120	246	0	LVH	96	Y	2.2	Down	1
749	54	M	NAP	150	232	0	LVH	165	N	1.6	Up	0
750	46	F	NAP	142	177	0	LVH	160	Y	1.4	Down	0
751	67	F	NAP	152	277	0	Normal	172	N	0.0	Up	0
752	56	M	ASY	125	249	1	LVH	144	Y	1.2	Flat	1
753	34	F	ATA	118	210	0	Normal	192	N	0.7	Up	0
754	57	M	ASY	132	207	0	Normal	168	Y	0.0	Up	0
755	64	M	ASY	145	212	0	LVH	132	N	2.0	Flat	1
756	59	M	ASY	138	271	0	LVH	182	N	0.0	Up	0
757	50	M	NAP	140	233	0	Normal	163	N	0.6	Flat	1
758	51	M	TA	125	213	0	LVH	125	Y	1.4	Up	0
759	54	M	ATA	192	283	0	LVH	195	N	0.0	Up	1
760	53	M	ASY	123	282	0	Normal	95	Y	2.0	Flat	1
761	52	M	ASY	112	230	0	Normal	160	N	0.0	Up	1
762	40	M	ASY	110	167	0	LVH	114	Y	2.0	Flat	1
763	58	M	NAP	132	224	0	LVH	173	N	3.2	Up	1
764	41	F	NAP	112	268	0	LVH	172	Y	0.0	Up	0
765	41	M	NAP	112	250	0	Normal	179	N	0.0	Up	0
766	50	F	NAP	120	219	0	Normal	158	N	1.6	Flat	0
767	54	F	NAP	108	267	0	LVH	167	N	0.0	Up	0
768	64	F	ASY	130	303	0	Normal	122	N	2.0	Flat	0
769	51	F	NAP	130	256	0	LVH	149	N	0.5	Up	0
770	46	F	ATA	105	204	0	Normal	172	N	0.0	Up	0
771	55	M	ASY	140	217	0	Normal	111	Y	5.6	Down	1
772	45	M	ATA	128	308	0	LVH	170	N	0.0	Up	0
773	56	M	TA	120	193	0	LVH	162	N	1.9	Flat	0
774	66	F	ASY	178	228	1	Normal	165	Y	1.0	Flat	1
775	38	M	TA	120	231	0	Normal	182	Y	3.8	Flat	1
776	62	F	ASY	150	244	0	Normal	154	Y	1.4	Flat	1
777	55	M	ATA	130	262	0	Normal	155	N	0.0	Up	0
778	58	M	ASY	128	259	0	LVH	130	Y	3.0	Flat	1
779	43	M	ASY	110	211	0	Normal	161	N	0.0	Up	0
780	64	F	ASY	180	325	0	Normal	154	Y	0.0	Up	0
781	50	F	ASY	110	254	0	LVH	159	N	0.0	Up	0
782	53	M	NAP	130	197	1	LVH	152	N	1.2	Down	0
783	45	F	ASY	138	236	0	LVH	152	Y	0.2	Flat	0
784	65	M	TA	138	282	1	LVH	174	N	1.4	Flat	1
785	69	M	TA	160	234	1	LVH	131	N	0.1	Flat	0
786	69	M	NAP	140	254	0	LVH	146	N	2.0	Flat	1
787	67	M	ASY	100	299	0	LVH	125	Y	0.9	Flat	1
788	68	F	NAP	120	211	0	LVH	115	N	1.5	Flat	0
789	34	M	TA	118	182	0	LVH	174	N	0.0	Up	0
790	62	F	ASY	138	294	1	Normal	106	N	1.9	Flat	1
791	51	M	ASY	140	298	0	Normal	122	Y	4.2	Flat	1
792	46	M	NAP	150	231	0	Normal	147	N	3.6	Flat	1
793	67	M	ASY	125	254	1	Normal	163	N	0.2	Flat	1
794	50	M	NAP	129	196	0	Normal	163	N	0.0	Up	0
795	42	M	NAP	120	240	1	Normal	194	N	0.8	Down	0
796	56	F	ASY	134	409	0	LVH	150	Y	1.9	Flat	1
797	41	M	ASY	110	172	0	LVH	158	N	0.0	Up	1
798	42	F	ASY	102	265	0	LVH	122	N	0.6	Flat	0
799	53	M	NAP	130	246	1	LVH	173	N	0.0	Up	0
800	43	M	NAP	130	315	0	Normal	162	N	1.9	Up	0
801	56	M	ASY	132	184	0	LVH	105	Y	2.1	Flat	1
802	52	M	ASY	108	233	1	Normal	147	N	0.1	Up	0
803	62	F	ASY	140	394	0	LVH	157	N	1.2	Flat	0
804	70	M	NAP	160	269	0	Normal	112	Y	2.9	Flat	1
805	54	M	ASY	140	239	0	Normal	160	N	1.2	Up	0
806	70	M	ASY	145	174	0	Normal	125	Y	2.6	Down	1
807	54	M	ATA	108	309	0	Normal	156	N	0.0	Up	0
808	35	M	ASY	126	282	0	LVH	156	Y	0.0	Up	1
809	48	M	NAP	124	255	1	Normal	175	N	0.0	Up	0
810	55	F	ATA	135	250	0	LVH	161	N	1.4	Flat	0
811	58	F	ASY	100	248	0	LVH	122	N	1.0	Flat	0
812	54	F	NAP	110	214	0	Normal	158	N	1.6	Flat	0
813	69	F	TA	140	239	0	Normal	151	N	1.8	Up	0
814	77	M	ASY	125	304	0	LVH	162	Y	0.0	Up	1
815	68	M	NAP	118	277	0	Normal	151	N	1.0	Up	0
816	58	M	ASY	125	300	0	LVH	171	N	0.0	Up	1
817	60	M	ASY	125	258	0	LVH	141	Y	2.8	Flat	1
818	51	M	ASY	140	299	0	Normal	173	Y	1.6	Up	1
819	55	M	ASY	160	289	0	LVH	145	Y	0.8	Flat	1
820	52	M	TA	152	298	1	Normal	178	N	1.2	Flat	0
821	60	F	NAP	102	318	0	Normal	160	N	0.0	Up	0
822	58	M	NAP	105	240	0	LVH	154	Y	0.6	Flat	0
823	64	M	NAP	125	309	0	Normal	131	Y	1.8	Flat	1
824	37	M	NAP	130	250	0	Normal	187	N	3.5	Down	0
825	59	M	TA	170	288	0	LVH	159	N	0.2	Flat	1
826	51	M	NAP	125	245	1	LVH	166	N	2.4	Flat	0
827	43	F	NAP	122	213	0	Normal	165	N	0.2	Flat	0
828	58	M	ASY	128	216	0	LVH	131	Y	2.2	Flat	1
829	29	M	ATA	130	204	0	LVH	202	N	0.0	Up	0
830	41	F	ATA	130	204	0	LVH	172	N	1.4	Up	0
831	63	F	NAP	135	252	0	LVH	172	N	0.0	Up	0
832	51	M	NAP	94	227	0	Normal	154	Y	0.0	Up	0
833	54	M	NAP	120	258	0	LVH	147	N	0.4	Flat	0
834	44	M	ATA	120	220	0	Normal	170	N	0.0	Up	0
835	54	M	ASY	110	239	0	Normal	126	Y	2.8	Flat	1
836	65	M	ASY	135	254	0	LVH	127	N	2.8	Flat	1
837	57	M	NAP	150	168	0	Normal	174	N	1.6	Up	0
838	63	M	ASY	130	330	1	LVH	132	Y	1.8	Up	1
839	35	F	ASY	138	183	0	Normal	182	N	1.4	Up	0
840	41	M	ATA	135	203	0	Normal	132	N	0.0	Flat	0
841	62	F	NAP	130	263	0	Normal	97	N	1.2	Flat	1
842	43	F	ASY	132	341	1	LVH	136	Y	3.0	Flat	1
843	58	F	TA	150	283	1	LVH	162	N	1.0	Up	0
844	52	M	TA	118	186	0	LVH	190	N	0.0	Flat	0
845	61	F	ASY	145	307	0	LVH	146	Y	1.0	Flat	1
846	39	M	ASY	118	219	0	Normal	140	N	1.2	Flat	1
847	45	M	ASY	115	260	0	LVH	185	N	0.0	Up	0
848	52	M	ASY	128	255	0	Normal	161	Y	0.0	Up	1
849	62	M	NAP	130	231	0	Normal	146	N	1.8	Flat	0
850	62	F	ASY	160	164	0	LVH	145	N	6.2	Down	1
851	53	F	ASY	138	234	0	LVH	160	N	0.0	Up	0
852	43	M	ASY	120	177	0	LVH	120	Y	2.5	Flat	1
853	47	M	NAP	138	257	0	LVH	156	N	0.0	Up	0
854	52	M	ATA	120	325	0	Normal	172	N	0.2	Up	0
855	68	M	NAP	180	274	1	LVH	150	Y	1.6	Flat	1
856	39	M	NAP	140	321	0	LVH	182	N	0.0	Up	0
857	53	F	ASY	130	264	0	LVH	143	N	0.4	Flat	0
858	62	F	ASY	140	268	0	LVH	160	N	3.6	Down	1
859	51	F	NAP	140	308	0	LVH	142	N	1.5	Up	0
860	60	M	ASY	130	253	0	Normal	144	Y	1.4	Up	1
861	65	M	ASY	110	248	0	LVH	158	N	0.6	Up	1
862	65	F	NAP	155	269	0	Normal	148	N	0.8	Up	0
863	60	M	NAP	140	185	0	LVH	155	N	3.0	Flat	1
864	60	M	ASY	145	282	0	LVH	142	Y	2.8	Flat	1
865	54	M	ASY	120	188	0	Normal	113	N	1.4	Flat	1
866	44	M	ATA	130	219	0	LVH	188	N	0.0	Up	0
867	44	M	ASY	112	290	0	LVH	153	N	0.0	Up	1
868	51	M	NAP	110	175	0	Normal	123	N	0.6	Up	0
869	59	M	NAP	150	212	1	Normal	157	N	1.6	Up	0
870	71	F	ATA	160	302	0	Normal	162	N	0.4	Up	0
871	61	M	NAP	150	243	1	Normal	137	Y	1.0	Flat	0
872	55	M	ASY	132	353	0	Normal	132	Y	1.2	Flat	1
873	64	M	NAP	140	335	0	Normal	158	N	0.0	Up	1
874	43	M	ASY	150	247	0	Normal	171	N	1.5	Up	0
875	58	F	NAP	120	340	0	Normal	172	N	0.0	Up	0
876	60	M	ASY	130	206	0	LVH	132	Y	2.4	Flat	1
877	58	M	ATA	120	284	0	LVH	160	N	1.8	Flat	1
878	49	M	ATA	130	266	0	Normal	171	N	0.6	Up	0
879	48	M	ATA	110	229	0	Normal	168	N	1.0	Down	1
880	52	M	NAP	172	199	1	Normal	162	N	0.5	Up	0
881	44	M	ATA	120	263	0	Normal	173	N	0.0	Up	0
882	56	F	ATA	140	294	0	LVH	153	N	1.3	Flat	0
883	57	M	ASY	140	192	0	Normal	148	N	0.4	Flat	0
884	67	M	ASY	160	286	0	LVH	108	Y	1.5	Flat	1
885	53	F	NAP	128	216	0	LVH	115	N	0.0	Up	0
886	52	M	NAP	138	223	0	Normal	169	N	0.0	Up	0
887	43	M	ASY	132	247	1	LVH	143	Y	0.1	Flat	1
888	52	M	ASY	128	204	1	Normal	156	Y	1.0	Flat	1
889	59	M	TA	134	204	0	Normal	162	N	0.8	Up	1
890	64	M	TA	170	227	0	LVH	155	N	0.6	Flat	0
891	66	F	NAP	146	278	0	LVH	152	N	0.0	Flat	0
892	39	F	NAP	138	220	0	Normal	152	N	0.0	Flat	0
893	57	M	ATA	154	232	0	LVH	164	N	0.0	Up	1
894	58	F	ASY	130	197	0	Normal	131	N	0.6	Flat	0
895	57	M	ASY	110	335	0	Normal	143	Y	3.0	Flat	1
896	47	M	NAP	130	253	0	Normal	179	N	0.0	Up	0
897	55	F	ASY	128	205	0	ST	130	Y	2.0	Flat	1
898	35	M	ATA	122	192	0	Normal	174	N	0.0	Up	0
899	61	M	ASY	148	203	0	Normal	161	N	0.0	Up	1
900	58	M	ASY	114	318	0	ST	140	N	4.4	Down	1
901	58	F	ASY	170	225	1	LVH	146	Y	2.8	Flat	1
902	58	M	ATA	125	220	0	Normal	144	N	0.4	Flat	0
903	56	M	ATA	130	221	0	LVH	163	N	0.0	Up	0
904	56	M	ATA	120	240	0	Normal	169	N	0.0	Down	0
905	67	M	NAP	152	212	0	LVH	150	N	0.8	Flat	1
906	55	F	ATA	132	342	0	Normal	166	N	1.2	Up	0
907	44	M	ASY	120	169	0	Normal	144	Y	2.8	Down	1
908	63	M	ASY	140	187	0	LVH	144	Y	4.0	Up	1
909	63	F	ASY	124	197	0	Normal	136	Y	0.0	Flat	1
910	41	M	ATA	120	157	0	Normal	182	N	0.0	Up	0
911	59	M	ASY	164	176	1	LVH	90	N	1.0	Flat	1
912	57	F	ASY	140	241	0	Normal	123	Y	0.2	Flat	1
913	45	M	TA	110	264	0	Normal	132	N	1.2	Flat	1
914	68	M	ASY	144	193	1	Normal	141	N	3.4	Flat	1
915	57	M	ASY	130	131	0	Normal	115	Y	1.2	Flat	1
916	57	F	ATA	130	236	0	LVH	174	N	0.0	Flat	1
917	38	M	NAP	138	175	0	Normal	173	N	0.0	Up	0

Data Preparation - Step 1

This data set has 12 columns and there's no columns that can specifically identifying a patient.So no need of removing a column.

#Get features name
df.columns

Index(['Age', 'Sex', 'ChestPainType', 'RestingBP', 'Cholesterol', 'FastingBS',
       'RestingECG', 'MaxHR', 'ExerciseAngina', 'Oldpeak', 'ST_Slope',
       'HeartDisease'],
      dtype='object')

## Display a description of the DataFrame with summary statistics
print("Describe our DataFrame :\n")
# Prints a summary of numerical columns with statistics such as count, mean, standard deviation, min, and max,
# rounding the results to two decimal places for readability
print(df.describe().round(2))
# Display DataFrame information
print("\nDataframe information:\n")
# Prints information about the DataFrame, including the index, column names, non-null counts, and data types
print(df.info())

Describe our DataFrame :

          Age  RestingBP  Cholesterol  FastingBS   MaxHR  Oldpeak  \
count  918.00     918.00       918.00     918.00  918.00   918.00   
mean    53.51     132.40       198.80       0.23  136.81     0.89   
std      9.43      18.51       109.38       0.42   25.46     1.07   
min     28.00       0.00         0.00       0.00   60.00    -2.60   
25%     47.00     120.00       173.25       0.00  120.00     0.00   
50%     54.00     130.00       223.00       0.00  138.00     0.60   
75%     60.00     140.00       267.00       0.00  156.00     1.50   
max     77.00     200.00       603.00       1.00  202.00     6.20   

       HeartDisease  
count        918.00  
mean           0.55  
std            0.50  
min            0.00  
25%            0.00  
50%            1.00  
75%            1.00  
max            1.00  

Dataframe information:

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 918 entries, 0 to 917
Data columns (total 12 columns):
 #   Column          Non-Null Count  Dtype  
---  ------          --------------  -----  
 0   Age             918 non-null    int64  
 1   Sex             918 non-null    object 
 2   ChestPainType   918 non-null    object 
 3   RestingBP       918 non-null    int64  
 4   Cholesterol     918 non-null    int64  
 5   FastingBS       918 non-null    int64  
 6   RestingECG      918 non-null    object 
 7   MaxHR           918 non-null    int64  
 8   ExerciseAngina  918 non-null    object 
 9   Oldpeak         918 non-null    float64
 10  ST_Slope        918 non-null    object 
 11  HeartDisease    918 non-null    int64  
dtypes: float64(1), int64(6), object(5)
memory usage: 86.2+ KB
None

#Checking the missing value
df.isnull().sum()

Age               0
Sex               0
ChestPainType     0
RestingBP         0
Cholesterol       0
FastingBS         0
RestingECG        0
MaxHR             0
ExerciseAngina    0
Oldpeak           0
ST_Slope          0
HeartDisease      0
dtype: int64

#Visualizing the missing value by seaborn library
sns.heatmap(df.isnull(),cmap = 'magma',cbar = False);

Data Cleanup

# Clean and replace invalid entries in the DataFrame
df = df.map(
  lambda x: x if x is np.NaN or not \
    isinstance(x, str) else str(x).strip('_')).replace(['', 'nan', '!@9#%8', '#F%$D@*&8'], np.NaN)
# Get the dimensions of the DataFrame (rows, columns)
df.shape

(918, 12)

# Display the data types of each column in the DataFrame
df.dtypes

Age                 int64
Sex                object
ChestPainType      object
RestingBP           int64
Cholesterol         int64
FastingBS           int64
RestingECG         object
MaxHR               int64
ExerciseAngina     object
Oldpeak           float64
ST_Slope           object
HeartDisease        int64
dtype: object

Data Preparation - Step 2

Oldpeak column shouldn't be an Object type as it has all numeric values. Let's convert that column to a Numeric Type

# Convert the 'Oldpeak' column to a numeric data type, handling errors by setting invalid parsing as NaN
df['Oldpeak'] = pd.to_numeric(df['Oldpeak'], errors='coerce').astype('float')

df.dtypes

Age                 int64
Sex                object
ChestPainType      object
RestingBP           int64
Cholesterol         int64
FastingBS           int64
RestingECG         object
MaxHR               int64
ExerciseAngina     object
Oldpeak           float64
ST_Slope           object
HeartDisease        int64
dtype: object

Missing Values

# Calculate the sum of missing values in each column
missing_values_sum = df.isna().sum()

# Calculate the percentage of missing values for each column
total_rows = df.shape[0]
missing_values_percentage = (missing_values_sum / total_rows) * 100

# Get the data type of the column as well
column_datatypes = df.dtypes

# Create a DataFrame to display the results
missing_info = pd.DataFrame({
    'Data Type': column_datatypes,
    'Missing Values': missing_values_sum,
    'Percentage Missing (%)': missing_values_percentage
})

# Display the missing values sum and percentage
print(missing_info)

               Data Type  Missing Values  Percentage Missing (%)
Age                int64               0                     0.0
Sex               object               0                     0.0
ChestPainType     object               0                     0.0
RestingBP          int64               0                     0.0
Cholesterol        int64               0                     0.0
FastingBS          int64               0                     0.0
RestingECG        object               0                     0.0
MaxHR              int64               0                     0.0
ExerciseAngina    object               0                     0.0
Oldpeak          float64               0                     0.0
ST_Slope          object               0                     0.0
HeartDisease       int64               0                     0.0

# Create a bar plot for missing values percentage 
plt.figure(figsize=(10, 5))

missing_values_percentage.plot(kind='bar', color='lightcoral')
plt.title('Missing Values Percentage')
plt.xlabel('Columns')
plt.ylabel('Percentage (%)')

# Adjust spacing between plots
plt.tight_layout()

# Show the plot
plt.show()

According to the above results, no missing values were found in our dataset.

Categorical Data:

#Encoding categorical Data
df['Sex'] = df['Sex'].replace({'M': 1, 'F': 2})
df['ChestPainType'] = df['ChestPainType'].replace({'ATA': 1, 'NAP': 2, 'ASY': 3, 'TA': 4})
df['FastingBS'] = df['FastingBS'].replace({0: 1, 1: 2})
df['RestingECG'] = df['RestingECG'].replace({'Normal': 1, 'ST': 2, 'LVH': 3})
df['ExerciseAngina'] = df['ExerciseAngina'].replace({'N': 1, 'Y': 2})
df['ST_Slope'] = df['ST_Slope'].replace({'Up': 1, 'Flat': 2, 'Down': 3})
df['HeartDisease'] = df['HeartDisease'].replace({1: 1, 0: 2})

#separate categorical data/numerical data
df_num = df[['Age', 'RestingBP', 'Cholesterol', 'MaxHR', 'Oldpeak']]
df_cat = df[['Sex', 'ChestPainType','FastingBS', 'RestingECG', 'ExerciseAngina', 'ST_Slope', 'HeartDisease']]

df_num_name = ['Age', 'RestingBP', 'Cholesterol', 'MaxHR', 'Oldpeak']
df_cat_name = ['Sex', 'ChestPainType','FastingBS', 'RestingECG', 'ExerciseAngina', 'ST_Slope', 'HeartDisease']

#Easier way to separate numerical value and categorical value if we have many features 
col = list(df.columns)
categorical_features = []
numerical_features = []
for i in col:
    if len(df[i].unique()) > 6:
        numerical_features.append(i)
    else:
        categorical_features.append(i)

print('Categorical Features :',*categorical_features,'\n')
print('Numerical Features :',*numerical_features)

Categorical Features : Sex ChestPainType FastingBS RestingECG ExerciseAngina ST_Slope HeartDisease 

Numerical Features : Age RestingBP Cholesterol MaxHR Oldpeak

Outliers

for feature in df_num_name:
    plt.figure(figsize=(4,5))
    plt.boxplot(df[feature])
    plt.title(f'{feature}')
    plt.show()

#Detecting outliers
def find_outliers_IQR(df):

   q1=df.quantile(0.25)

   q3=df.quantile(0.75)

   IQR=q3-q1

   outliers = df[((df<(q1-1.5*IQR)) | (df>(q3+1.5*IQR)))]

   return outliers

for char in df_num_name :
    outliers = find_outliers_IQR(df[str(char)])
    
    print(f"-Check {char} outliers ")

    print('number of outliers: '+ str(len(outliers)))

    print('max outlier value: '+ str(outliers.max()))

    print('min outlier value: '+ str(outliers.min())+ '\n')

-Check Age outliers 
number of outliers: 0
max outlier value: nan
min outlier value: nan

-Check RestingBP outliers 
number of outliers: 28
max outlier value: 200
min outlier value: 0

-Check Cholesterol outliers 
number of outliers: 183
max outlier value: 603
min outlier value: 0

-Check MaxHR outliers 
number of outliers: 2
max outlier value: 63
min outlier value: 60

-Check Oldpeak outliers 
number of outliers: 16
max outlier value: 6.2
min outlier value: -2.6

# Define a function to generate a histogram for a specific column
def generate_histogram(data, column_name):
    plt.figure(figsize=(6, 4))
    sns.histplot(data[column_name], bins=20, kde=True)  # Adjust the number of bins as needed
    
    # Customize the y-axis tick labels
    plt.gca().get_yaxis().get_major_formatter().set_scientific(False)
    
    plt.xlabel(column_name)
    plt.ylabel('Frequency')
    plt.title(f'Histogram of {column_name}')
    plt.show()

# Define a function to generate a box plot for a specific column
def generate_boxplot(data, column_name):
    plt.figure(figsize=(6, 4))
    sns.boxplot(y=data[column_name])
    
    # Customize the y-axis tick labels
    plt.gca().get_yaxis().get_major_formatter().set_scientific(False)
    
    plt.ylabel(column_name)
    plt.title(f'Box Plot of {column_name}')
    plt.show()

Age Column Data Analysis

# Generate and display a histogram for the 'Age' column in the DataFrame
generate_histogram(df, 'Age')
# Generate and display a boxplot for the 'Age' column in the DataFrame
generate_boxplot(df, 'Age')

Cholesterol Column Data Analysis

# Generate and display a histogram for the 'Cholesterol' column in the DataFrame
generate_histogram(df, 'Cholesterol')
# Generate and display a boxplot for the 'Cholesterol' column in the DataFrame
generate_boxplot(df, 'Cholesterol')

As per the above diagram, the Cholesterol column contains data that doesn’t seems to be practical. We must exclude those data considering the optimal Cholesterol level to be 60 to 700 .

# Checking the number of Zeros present in the Cholesterol
print(f" original shape of Cholesterol : {df.Cholesterol.shape}")

ziro_Cholesterol = df[df['Cholesterol'] == 0]
print(f" zero values of Cholesterol : {ziro_Cholesterol.shape}")

non_ziro_Cholesterol = df[df['Cholesterol'] != 0]
print(f" non zero values of Cholesterol : {non_ziro_Cholesterol.shape}")

 original shape of Cholesterol : (918,)
 zero values of Cholesterol : (172, 12)
 non zero values of Cholesterol : (746, 12)

The cholesterol value cannot be zero. So, zeros must be filled with the median value

## Converting Zero Value of Cholesterol to NaN Value
df.loc[df['Cholesterol'] == 0,'Cholesterol'] = np.nan
df.head(5)

	Age	Sex	ChestPainType	RestingBP	Cholesterol	FastingBS	RestingECG	MaxHR	ExerciseAngina	Oldpeak	ST_Slope	HeartDisease
0	40	1	1	140	289.0	1	1	172	1	0.0	1	2
1	49	2	2	160	180.0	1	1	156	1	1.0	2	1
2	37	1	1	130	283.0	1	2	98	1	0.0	1	2
3	48	2	3	138	214.0	1	1	108	2	1.5	2	1
4	54	1	2	150	195.0	1	1	122	1	0.0	1	2

## NaN value of Cholesterol is filled with median value
df["Cholesterol"] = df["Cholesterol"].fillna(df["Cholesterol"].median())
df["Cholesterol"]

0      289.0
1      180.0
2      283.0
3      214.0
4      195.0
5      339.0
6      237.0
7      208.0
8      207.0
9      284.0
10     211.0
11     164.0
12     204.0
13     234.0
14     211.0
15     273.0
16     196.0
17     201.0
18     248.0
19     267.0
20     223.0
21     184.0
22     201.0
23     288.0
24     215.0
25     209.0
26     260.0
27     284.0
28     468.0
29     188.0
30     518.0
31     167.0
32     224.0
33     172.0
34     186.0
35     254.0
36     306.0
37     250.0
38     177.0
39     227.0
40     230.0
41     294.0
42     264.0
43     259.0
44     175.0
45     318.0
46     223.0
47     216.0
48     340.0
49     289.0
50     233.0
51     205.0
52     224.0
53     245.0
54     180.0
55     194.0
56     270.0
57     213.0
58     365.0
59     342.0
60     253.0
61     254.0
62     224.0
63     277.0
64     202.0
65     260.0
66     297.0
67     225.0
68     246.0
69     412.0
70     265.0
71     215.0
72     182.0
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Name: Cholesterol, dtype: float64

df = df[(df['Cholesterol'] >= 60) & (df['Cholesterol'] <= 700)]

df.shape

(918, 12)

# Regenerate and display a histogram for the 'Cholesterol' column in the DataFrame
generate_histogram(df, 'Cholesterol')
# Regenerate and display a boxplot for the 'Cholesterol' column in the DataFrame
generate_boxplot(df, 'Cholesterol')

Resting Blood Pressure Column Data Analysis

# Regenerate and display a histogram for the 'RestingBP' column in the DataFrame
generate_histogram(df, 'RestingBP')
# Regenerate and display a boxplot for the 'RestingBP' column in the DataFrame
generate_boxplot(df, 'RestingBP')

Based on the histogram and boxplot, it appears that RestingBP (Resting Blood Pressure) should not contain zero values. Let’s identify and remove any rows where RestingBP is zero, as such values likely represent errors or missing data

#Checking the smallest and largest RestingBP values in dataset
print(f"smallest values :\n{df.RestingBP.nsmallest(3)}")
print(f"largest values :\n{df.RestingBP.nlargest(3)}")

smallest values :
449     0
314    80
227    92
Name: RestingBP, dtype: int64
largest values :
241    200
365    200
399    200
Name: RestingBP, dtype: int64

#Normally Blood Pressure can not be zero value , but in this dataset we have 1 sample
ziro_RestingBP = df[df['RestingBP'] == 0]
ziro_RestingBP

	Age	Sex	ChestPainType	RestingBP	Cholesterol	FastingBS	RestingECG	MaxHR	ExerciseAngina	Oldpeak	ST_Slope	HeartDisease
449	55	1	2	0	237.0	1	1	155	1	1.5	2	1

#We should remove this row 
df = df.drop(df[(df['RestingBP'] == 0)].index) 

# Regenerate and display histogram and boxplot for the 'RestingBP' column in the DataFrame
generate_histogram(df, 'RestingBP')
generate_boxplot(df, 'RestingBP')

Oldpeak Column Data Analysis

# Generate and display histogram and boxplot for the 'Oldpeak' column in the DataFrame
generate_histogram(df, 'Oldpeak')
generate_boxplot(df, 'Oldpeak')

#Checking the smallest and largest Oldpeak values in dataset
print(f"smallest values :\n{df.Oldpeak.nsmallest(3)}")
print(f"largest values :\n{df.Oldpeak.nlargest(3)}")

smallest values :
324   -2.6
413   -2.0
325   -1.5
Name: Oldpeak, dtype: float64
largest values :
850    6.2
771    5.6
166    5.0
Name: Oldpeak, dtype: float64

#According to the Oldpeak value range we haven`t minus value , but in this dataset we have 13 samples
ziro_Oldpeak = df[df['Oldpeak'] < 0]
ziro_Oldpeak

	Age	Sex	ChestPainType	RestingBP	Cholesterol	FastingBS	RestingECG	MaxHR	ExerciseAngina	Oldpeak	ST_Slope	HeartDisease
321	63	1	3	100	237.0	2	1	109	1	-0.9	2	1
324	46	1	3	100	237.0	2	2	133	1	-2.6	2	1
325	42	1	3	105	237.0	2	1	128	2	-1.5	3	1
326	45	1	2	110	237.0	1	1	138	1	-0.1	1	2
331	56	1	3	115	237.0	2	2	82	1	-1.0	1	1
332	38	1	2	100	237.0	1	1	179	1	-1.1	1	2
334	51	1	3	130	237.0	2	1	170	1	-0.7	1	1
335	62	1	4	120	237.0	2	3	134	1	-0.8	2	1
352	56	1	3	120	237.0	1	2	100	2	-1.0	3	1
407	62	1	3	115	237.0	2	1	72	2	-0.5	2	1
413	56	1	2	125	237.0	2	1	98	1	-2.0	2	1
421	66	1	2	120	237.0	1	2	120	1	-0.5	1	2
512	35	1	2	123	161.0	1	2	153	1	-0.1	1	2

#We should remove those rows 
df = df.drop(df[(df['Oldpeak'] < 0)].index)

# Regenerate and display histogram and boxplot for the 'Oldpeak' column in the DataFrame
generate_histogram(df, 'Oldpeak')
generate_boxplot(df, 'Oldpeak')

MaxHR Column Data Analysis

# Generate and display histogram and boxplot for the 'MaxHR' column in the DataFrame
generate_histogram(df, 'MaxHR')
generate_boxplot(df, 'MaxHR')

Unique and Nununique value:

#Showing the unique and nununique values
df.nunique()

Age                50
Sex                 2
ChestPainType       4
RestingBP          66
Cholesterol       221
FastingBS           2
RestingECG          3
MaxHR             119
ExerciseAngina      2
Oldpeak            43
ST_Slope            3
HeartDisease        2
dtype: int64

#Below the red horizontal line ,all features are contain categorical data
df.nunique().plot(kind='bar')
plt.title('No of unique values in the dataset')
plt.hlines(y=6, xmin=-2, xmax=15, color='red')
plt.show()

for i in range(0,7):
    print(f"\nShow the unique value of {df_cat_name[i]} column : {pd.unique(df[df_cat_name[i]])}\n")  

Show the unique value of Sex column : [1 2]


Show the unique value of ChestPainType column : [1 2 3 4]


Show the unique value of FastingBS column : [1 2]


Show the unique value of RestingECG column : [1 2 3]


Show the unique value of ExerciseAngina column : [1 2]


Show the unique value of ST_Slope column : [1 2 3]


Show the unique value of HeartDisease column : [2 1]

i = 0
while i < 7 :
    if i < 6 :
        plt.figure(figsize=(8,5))
        plt.subplot(1, 3, 1)
        sns.countplot(x=df_cat_name[i] , data=df, hue=df_cat_name[i], palette="deep", legend=False)
        plt.xlabel(str(df_cat_name[i]))
        i+=1

        plt.subplot(1, 3, 2)
        sns.countplot(x=df_cat_name[i] , data=df, hue=df_cat_name[i], palette="deep", legend=False)
        plt.xlabel(str(df_cat_name[i]))
        i+=1
    
        plt.subplot(1, 3, 3)
        sns.countplot(x=df_cat_name[i] , data=df, hue=df_cat_name[i], palette="deep", legend=False)
        plt.xlabel(str(df_cat_name[i]))
        i+=1
    elif i == 6 :
        plt.figure(figsize=(2.4,5))
        plt.subplot(1, 1, 1)
        sns.countplot(x=df_cat_name[i] , data=df, hue=df_cat_name[i], palette="deep", legend=False)
        plt.xlabel(str(df_cat_name[i]))
        i+=1   
        
        plt.subplots_adjust(left=0.1,
                    bottom=0.1, 
                    right=0.9, 
                    top=0.9, 
                    wspace=0.4, 
                    hspace=0.4)
    
        plt.show()

Pie Chart

#Count of unique value in each feature
for c in range(6) :
    print(f"\nCount of {df_cat_name[c]} :\n{df[df_cat_name[c]].value_counts()}\n\n------------")

Count of Sex :
Sex
1    711
2    193
Name: count, dtype: int64

------------

Count of ChestPainType :
ChestPainType
3    489
2    197
1    173
4     45
Name: count, dtype: int64

------------

Count of FastingBS :
FastingBS
1    698
2    206
Name: count, dtype: int64

------------

Count of RestingECG :
RestingECG
1    544
3    187
2    173
Name: count, dtype: int64

------------

Count of ExerciseAngina :
ExerciseAngina
1    536
2    368
Name: count, dtype: int64

------------

Count of ST_Slope :
ST_Slope
2    454
1    389
3     61
Name: count, dtype: int64

------------

# Define labels and sizes for the 'Sex' category in the pie chart
sex_label = ['Male','Female']
sex_size = [725,193]
sex_explode = (0,0)
# Set up the figure for the pie chart
plt.figure(figsize=(8,5),facecolor='lightgray')
# Create a pie chart with the provided data
plt.pie( sex_size , explode=sex_explode , labels=sex_label ,shadow=True ,
        autopct='%1.2f%%' , startangle=90 , colors=['lime','crimson'])
plt.title("Count of Sex",fontsize=25,color='maroon')
plt.legend()

<matplotlib.legend.Legend at 0x1d6d6001010>

# Define labels and sizes for the 'FastingBS' category in the pie chart
FastingBS_label = ['1: if FBS>120','0: otherwise']
FastingBS_size = [704,214]
FastingBS_explode = (0,0)

# Set up the figure for the pie chart
plt.figure(figsize=(8,5),facecolor='lightgray')
# Create a pie chart with the provided data
plt.pie( FastingBS_size , explode=FastingBS_explode , labels=FastingBS_label ,shadow=True ,
        autopct='%1.2f%%' , startangle=90 , colors=['lime','crimson'])
plt.title("Count of FastingBS",fontsize=25,color='maroon')
plt.legend()

<matplotlib.legend.Legend at 0x1d6d5fb1010>

# Define labels and sizes for the 'ChestPainType' category in the pie chart
ChestPainType_label = ['ATA', 'NAP', 'ASY', 'TA']
ChestPainType_size = [173, 203, 496, 46]
ChestPainType_explode = (0, 0, 0, 0)
# Set up the figure for the pie chart
plt.figure(figsize=(8,6),facecolor='lightgray')
# Create a pie chart with the provided data
plt.pie( ChestPainType_size , explode=ChestPainType_explode , labels=ChestPainType_label ,shadow=True ,
        autopct='%1.2f%%' , startangle=90, colors= ['lime','crimson', 'slateblue', 'deeppink'])
plt.title("count of ChestPainType",fontsize=25,color='maroon')
plt.legend()

<matplotlib.legend.Legend at 0x1d6d61d7fe0>

# Define labels and sizes for the 'RestingECG' category in the pie chart
RestingECG_label = ['Normal', 'ST', 'LVH']
RestingECG_size = [552, 178, 188]
RestingECG_explode = (0, 0, 0)
# Set up the figure for the pie chart
plt.figure(figsize=(8, 5),facecolor='lightgray')
# Create a pie chart with the provided data
plt.pie( RestingECG_size , explode=RestingECG_explode , labels=RestingECG_label ,shadow=True ,
        autopct='%1.2f%%' , startangle=90, colors=['lime', 'crimson', 'slateblue'])
plt.title("count of RestingECG",fontsize=25,color='maroon')
plt.legend()

<matplotlib.legend.Legend at 0x1d6d9ae9010>

# Define labels and sizes for the 'ExerciseAngina' category in the pie chart
ExerciseAngina_label = ['Yes', 'No']
ExerciseAngina_size = [547, 371]
ExerciseAngina_explode = (0, 0)
# Set up the figure for the pie chart
plt.figure(figsize=(8, 5),facecolor='lightgray')
# Create a pie chart with the provided data
plt.pie( ExerciseAngina_size , explode=ExerciseAngina_explode , labels=ExerciseAngina_label ,shadow=True ,
        autopct='%1.2f%%' , startangle=90, colors=['lime', 'crimson'])
plt.title("count of ExerciseAngina",fontsize=25,color='maroon')
plt.legend()

<matplotlib.legend.Legend at 0x1d6d870a7b0>

Catplot

for i in range(0,7) :
    if i < 6 :
         # Set up a new figure for the count plot with specified dimensions
        plt.figure(figsize=(3,4))
         # Create a categorical count plot using seaborn
        # 'HeartDisease' is plotted on the x-axis, and the hue is determined by the category defined in df_cat_name[i]
        sns.catplot(x="HeartDisease", hue=df_cat_name[i], kind="count",palette="pastel", edgecolor=".9",data=df)
        plt.show()
    elif i==6 :
        pass

<Figure size 300x400 with 0 Axes>

<Figure size 300x400 with 0 Axes>

<Figure size 300x400 with 0 Axes>

<Figure size 300x400 with 0 Axes>

<Figure size 300x400 with 0 Axes>

<Figure size 300x400 with 0 Axes>

Visualising numerical Data

# Set up a new figure for the count plot with specified dimensions
plt.figure(figsize=(10,5))
# Define bins for categorizing ages into intervals
bins=[0, 20, 40, 60, 80]
# Create a count plot using seaborn
# The x-axis represents age categories defined by the bins, and the hue represents the presence of heart disease
sns.countplot(x=pd.cut(df.Age,bins=bins),hue=df.HeartDisease)
plt.title("HeartDisease by Age", fontsize=15)
plt.show()

# Create a horizontal count plot using seaborn
# The y-axis represents the 'Sex' variable, and the hue distinguishes between those with and without heart disease
ax = sns.countplot(y="Sex", hue="HeartDisease", data=df)

Box Plot

# Set up the subplots grid
from plotly.subplots import make_subplots
fig = make_subplots(rows=2, cols=3, 
                    # Set the subplot titles
                    subplot_titles=['Age', 'RestingBP', 'Cholesterol','MaxHR','Oldpeak'])
#create boxplot visualization of numeric columns
fig.add_trace(go.Box(x=df.Age, name='', showlegend=False), row=1, col=1)
fig.add_trace(go.Box(x=df.RestingBP, name='', showlegend=False), row=1, col=2)
fig.add_trace(go.Box(x=df.Cholesterol, name='', showlegend=False), row=1, col=3)
fig.add_trace(go.Box(x=df.MaxHR, name='', showlegend=False), row=2, col=1)
fig.add_trace(go.Box(x=df.Oldpeak, name='', showlegend=False), row=2, col=2)

#config size
fig.update_layout(height=500, width=1100)
#show visualizations
fig.show()

Correlation

#Correlation between all features 
fig , ax = plt.subplots(figsize=(13,6))   
sns.heatmap(df.corr(), cmap="coolwarm", linecolor='white' , annot=True , linewidths=1 , ax=ax )

<Axes: >

# Calculate the absolute correlation coefficients between all numerical columns and 'HeartDisease'
pd.DataFrame(abs(df.corr()['HeartDisease'])).T.round(2)

	Age	Sex	ChestPainType	RestingBP	Cholesterol	FastingBS	RestingECG	MaxHR	ExerciseAngina	Oldpeak	ST_Slope	HeartDisease
HeartDisease	0.28	0.31	0.46	0.12	0.08	0.26	0.06	0.4	0.5	0.43	0.56	1.0

## Combining Data
df.agg(
    {
       "Age": ["min", "max", "median","mean", "skew", 'std'],
        "RestingBP": ["min", "max", "median", "mean","skew",'std'],
        "Cholesterol": ["min", "max", "median", "mean","skew",'std'],
        "Oldpeak": ["min", "max", "median", "mean","skew",'std'],
        "MaxHR": ["min", "max", "median", "mean","skew",'std']
    }
)

	Age	RestingBP	Cholesterol	Oldpeak	MaxHR
min	28.000000	80.000000	85.000000	0.000000	60.000000
max	77.000000	200.000000	603.000000	6.200000	202.000000
median	54.000000	130.000000	237.000000	0.600000	138.000000
mean	53.528761	132.806416	243.384956	0.913606	136.969027
skew	-0.193384	0.605859	1.435895	1.140450	-0.136971
std	9.431648	17.952414	53.738632	1.047342	25.337263

Data Train and Test

x = df.drop('HeartDisease', axis=1)
y = df.HeartDisease

from sklearn.model_selection import train_test_split
x_train, x_test, y_train, y_test = train_test_split(x, y, test_size=0.3, random_state=21)

Feature Scaling with Normalization method

from sklearn.preprocessing import MinMaxScaler
scaler = MinMaxScaler()
x_train = scaler.fit_transform(x_train) 
x_test = scaler.fit_transform(x_test)

x_train = pd.DataFrame(x_train, columns=x.columns)
x_test = pd.DataFrame(x_test, columns=x.columns)

print("x train : \n")
print(x_train.head())
print("x test : \n")
print(x_test.head())

x train : 

        Age  Sex  ChestPainType  RestingBP  Cholesterol  FastingBS  \
0  0.428571  0.0       0.666667   0.500000     0.311065        0.0   
1  0.224490  0.0       0.000000   0.333333     0.248434        0.0   
2  0.714286  0.0       0.666667   0.500000     0.365344        0.0   
3  0.530612  0.0       0.666667   1.000000     0.235908        0.0   
4  0.530612  0.0       0.000000   0.933333     0.413361        0.0   

   RestingECG     MaxHR  ExerciseAngina   Oldpeak  ST_Slope  
0         0.0  0.553957             1.0  0.161290       0.5  
1         0.0  0.589928             0.0  0.000000       0.0  
2         0.5  0.352518             1.0  0.483871       0.5  
3         0.0  0.568345             1.0  0.322581       0.5  
4         1.0  0.949640             0.0  0.000000       0.0  
x test : 

        Age  Sex  ChestPainType  RestingBP  Cholesterol  FastingBS  \
0  0.416667  0.0       0.666667   0.361702     0.210736        0.0   
1  0.479167  0.0       0.666667   0.361702     0.248509        0.0   
2  0.250000  0.0       0.666667   0.255319     0.272366        1.0   
3  0.500000  0.0       0.000000   0.255319     0.161034        0.0   
4  0.354167  0.0       0.666667   0.148936     0.202783        0.0   

   RestingECG     MaxHR  ExerciseAngina  Oldpeak  ST_Slope  
0         0.0  0.833333             0.0      0.0       0.5  
1         0.0  0.454545             1.0      0.4       0.5  
2         0.0  0.590909             1.0      0.2       0.5  
3         0.0  0.545455             0.0      0.0       0.0  
4         0.0  0.681818             1.0      0.0       0.5  

Modeling

#Spliting data in to training and testing sets 
x_train, x_test, y_train, y_test = train_test_split(x, y, test_size=0.3, random_state=34)

Logistic Regresssion

#Metrics
from sklearn import metrics
from sklearn.metrics import classification_report, confusion_matrix, f1_score, accuracy_score

from sklearn.linear_model import LogisticRegression

# Create and train a Logistic Regression model
LR_model = LogisticRegression(max_iter=1000)
LR_model.fit(x_train, y_train)

LogisticRegression(max_iter=1000)

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# Make predictions on the test set
LR_y_pred = LR_model.predict(x_test)

LR_train_preds = LR_model.predict(x_train)
LR_train_preds

array([1, 1, 1, 1, 2, 2, 1, 1, 2, 2, 1, 1, 1, 1, 2, 2, 2, 2, 1, 1, 2, 2,
       1, 2, 1, 1, 2, 1, 1, 1, 1, 2, 1, 1, 2, 1, 2, 2, 1, 1, 2, 2, 1, 2,
       2, 2, 1, 2, 2, 2, 1, 1, 1, 2, 2, 2, 1, 1, 1, 1, 1, 2, 2, 2, 2, 1,
       2, 1, 1, 1, 2, 1, 2, 1, 1, 2, 2, 2, 2, 1, 1, 1, 1, 1, 2, 1, 2, 1,
       1, 1, 2, 2, 1, 1, 1, 1, 1, 1, 1, 1, 2, 2, 1, 1, 1, 2, 1, 1, 1, 1,
       2, 1, 1, 1, 2, 1, 1, 2, 1, 1, 2, 2, 1, 1, 2, 1, 1, 2, 2, 2, 2, 2,
       1, 1, 1, 2, 2, 1, 1, 2, 1, 2, 2, 1, 2, 1, 1, 1, 2, 2, 1, 2, 2, 2,
       2, 1, 1, 1, 2, 2, 1, 1, 1, 2, 1, 2, 1, 1, 2, 2, 2, 1, 2, 1, 1, 1,
       2, 2, 1, 1, 1, 1, 1, 1, 1, 1, 2, 1, 1, 1, 1, 1, 1, 2, 1, 2, 2, 1,
       2, 2, 1, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 2, 1, 2, 2, 1, 2, 2, 2, 1,
       2, 1, 2, 1, 2, 1, 2, 2, 1, 1, 1, 2, 1, 2, 2, 1, 2, 2, 1, 1, 2, 2,
       2, 1, 2, 2, 2, 1, 2, 1, 1, 1, 2, 2, 1, 1, 1, 1, 1, 1, 2, 2, 1, 2,
       1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 1, 1, 2, 2, 1, 1, 2, 2, 2, 1, 2, 2,
       2, 2, 1, 2, 1, 2, 1, 1, 1, 1, 2, 1, 1, 2, 1, 2, 2, 2, 1, 1, 1, 1,
       2, 2, 2, 2, 1, 2, 2, 2, 1, 1, 1, 1, 1, 2, 2, 1, 1, 1, 1, 1, 1, 2,
       1, 1, 1, 1, 2, 2, 1, 1, 1, 1, 2, 2, 1, 2, 1, 1, 1, 1, 2, 1, 1, 1,
       1, 2, 2, 1, 1, 1, 1, 1, 1, 2, 2, 2, 1, 1, 2, 1, 1, 1, 2, 2, 1, 2,
       1, 1, 2, 1, 1, 2, 2, 2, 2, 2, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2,
       2, 2, 2, 1, 2, 2, 1, 1, 1, 2, 1, 1, 2, 1, 1, 1, 1, 1, 2, 2, 2, 1,
       2, 1, 2, 1, 1, 2, 1, 1, 1, 1, 1, 2, 2, 2, 1, 1, 2, 2, 2, 1, 1, 1,
       2, 2, 1, 1, 2, 2, 1, 1, 2, 1, 2, 2, 1, 2, 1, 1, 2, 1, 2, 1, 1, 1,
       2, 1, 1, 1, 2, 1, 1, 1, 2, 2, 2, 2, 1, 2, 1, 1, 1, 1, 2, 2, 2, 1,
       2, 1, 1, 1, 1, 2, 1, 1, 2, 1, 1, 1, 1, 2, 1, 1, 1, 1, 1, 1, 2, 1,
       1, 1, 2, 2, 1, 1, 2, 2, 1, 1, 2, 2, 2, 2, 1, 2, 1, 2, 2, 2, 1, 1,
       1, 1, 1, 2, 2, 1, 2, 1, 2, 2, 1, 2, 2, 1, 1, 1, 1, 2, 2, 2, 1, 2,
       1, 2, 1, 1, 1, 1, 1, 1, 2, 2, 2, 1, 1, 2, 1, 1, 2, 1, 2, 2, 1, 2,
       1, 2, 1, 1, 1, 2, 1, 2, 1, 1, 1, 2, 1, 1, 2, 1, 2, 2, 2, 2, 1, 2,
       2, 2, 2, 1, 1, 1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 1, 1, 2, 1, 1, 2, 1,
       1, 1, 2, 2, 2, 1, 1, 1, 2, 1, 1, 2, 1, 2, 2, 1], dtype=int64)

LR_test_preds = LR_model.predict(x_test)
LR_test_preds

array([1, 2, 2, 1, 2, 2, 2, 1, 1, 2, 2, 1, 2, 1, 1, 2, 1, 1, 1, 2, 2, 2,
       2, 2, 1, 1, 1, 1, 2, 2, 1, 2, 1, 1, 2, 1, 2, 2, 1, 2, 1, 1, 2, 2,
       1, 1, 2, 2, 1, 1, 1, 1, 2, 2, 2, 2, 1, 1, 1, 1, 2, 2, 1, 2, 1, 1,
       1, 1, 2, 2, 2, 2, 2, 1, 1, 2, 1, 1, 1, 1, 2, 2, 2, 1, 1, 1, 1, 2,
       2, 1, 1, 2, 1, 1, 1, 1, 1, 2, 2, 1, 1, 1, 1, 2, 2, 2, 2, 1, 2, 2,
       1, 2, 1, 1, 1, 1, 2, 2, 1, 2, 1, 1, 1, 2, 2, 2, 2, 1, 2, 1, 2, 2,
       2, 2, 1, 1, 2, 2, 1, 1, 1, 1, 1, 2, 2, 1, 2, 1, 1, 1, 2, 2, 1, 2,
       2, 2, 1, 1, 2, 2, 2, 1, 2, 2, 2, 1, 1, 1, 2, 2, 1, 2, 1, 1, 2, 1,
       1, 2, 1, 1, 1, 1, 1, 1, 2, 1, 1, 1, 2, 1, 1, 1, 1, 1, 2, 1, 1, 1,
       1, 2, 2, 1, 2, 2, 1, 1, 2, 1, 2, 1, 1, 1, 1, 1, 1, 2, 1, 1, 2, 2,
       2, 2, 2, 1, 1, 2, 1, 2, 1, 1, 2, 1, 2, 2, 1, 2, 2, 2, 2, 1, 1, 1,
       2, 1, 2, 2, 1, 1, 1, 2, 1, 2, 2, 1, 1, 2, 2, 2, 2, 2, 1, 1, 2, 2,
       1, 2, 1, 2, 2, 2, 2, 1], dtype=int64)

LR_train_preds_score = LR_model.score(x_train,y_train)
LR_train_preds_score

0.8465189873417721

LR_test_preds_score = LR_model.score(x_test,y_test)
LR_test_preds_score

0.8639705882352942

# Compute the confusion matrix
conf_matrix = confusion_matrix(y_test, LR_y_pred)

# Create a DataFrame for the confusion matrix (optional)
confusion_df = pd.DataFrame(conf_matrix, index=LR_model.classes_, columns=LR_model.classes_)

# Create a heatmap to visualize the confusion matrix
plt.figure(figsize=(8, 6))
sns.heatmap(confusion_df, annot=True, fmt='d', cmap='Blues', linewidths=.5, cbar=False)
plt.xlabel('Predicted')
plt.ylabel('Actual')
plt.title('Confusion Matrix')
plt.show()

# Evaluate the model
accuracy = accuracy_score(y_test, LR_y_pred)
classification_rep = classification_report(y_test, LR_y_pred)
confusion_mat = confusion_matrix(y_test, LR_y_pred)

# Print the results
print(f"Accuracy: {accuracy}")
print("Classification Report:\n", classification_rep)
print("Confusion Matrix:\n", confusion_mat)

Accuracy: 0.8639705882352942
Classification Report:
               precision    recall  f1-score   support

           1       0.88      0.86      0.87       147
           2       0.84      0.86      0.85       125

    accuracy                           0.86       272
   macro avg       0.86      0.86      0.86       272
weighted avg       0.86      0.86      0.86       272

Confusion Matrix:
 [[127  20]
 [ 17 108]]

Random Forest

# Create and train a Random Forest Classifier model
RFC_model = RandomForestClassifier()
RFC_model.fit(x_train, y_train)

RandomForestClassifier()

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# Make predictions on the test set
RFC_y_pred =RFC_model.predict(x_test)

RFC_train_preds = RFC_model.predict(x_train)
RFC_train_preds

array([2, 1, 1, 1, 2, 2, 1, 1, 2, 2, 2, 1, 1, 1, 2, 2, 2, 2, 1, 2, 2, 1,
       1, 1, 1, 1, 2, 2, 1, 1, 1, 1, 1, 1, 2, 1, 2, 2, 1, 1, 2, 2, 1, 1,
       1, 2, 1, 2, 2, 2, 1, 1, 1, 2, 2, 2, 2, 1, 1, 1, 1, 2, 2, 2, 2, 2,
       2, 1, 1, 1, 2, 1, 2, 1, 1, 1, 2, 1, 2, 1, 1, 2, 1, 1, 2, 1, 2, 1,
       1, 1, 1, 2, 1, 1, 1, 1, 1, 1, 1, 1, 2, 2, 1, 1, 1, 2, 1, 1, 1, 1,
       2, 2, 2, 1, 2, 1, 1, 2, 1, 1, 2, 1, 1, 1, 2, 1, 1, 1, 2, 2, 2, 2,
       1, 1, 1, 1, 2, 1, 2, 2, 2, 1, 2, 1, 2, 2, 1, 1, 2, 2, 1, 2, 2, 2,
       2, 1, 1, 1, 1, 2, 1, 1, 1, 2, 1, 2, 2, 1, 2, 2, 1, 1, 2, 1, 1, 2,
       2, 1, 2, 1, 1, 1, 1, 1, 1, 2, 2, 1, 1, 1, 1, 1, 2, 2, 1, 2, 1, 1,
       2, 2, 1, 1, 2, 2, 2, 1, 2, 1, 2, 1, 2, 2, 1, 2, 2, 1, 2, 2, 1, 1,
       2, 1, 2, 1, 2, 1, 2, 1, 1, 1, 2, 2, 1, 2, 2, 1, 2, 2, 1, 1, 2, 2,
       2, 1, 2, 2, 2, 1, 2, 1, 2, 2, 1, 2, 1, 1, 1, 1, 1, 1, 2, 2, 1, 2,
       1, 1, 2, 1, 2, 1, 1, 1, 2, 1, 1, 1, 2, 2, 1, 1, 1, 2, 2, 1, 2, 1,
       1, 2, 1, 2, 2, 2, 1, 1, 1, 1, 2, 2, 1, 2, 1, 1, 1, 2, 1, 1, 1, 1,
       2, 2, 2, 2, 2, 2, 2, 2, 1, 1, 2, 1, 1, 2, 2, 1, 1, 1, 2, 1, 1, 2,
       2, 1, 1, 1, 2, 1, 1, 1, 1, 1, 2, 2, 1, 1, 1, 1, 1, 1, 1, 2, 1, 1,
       2, 2, 2, 1, 1, 2, 2, 1, 1, 2, 2, 2, 1, 2, 2, 1, 1, 1, 2, 2, 1, 2,
       1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 2, 1, 1, 1, 1, 1, 1, 2, 1, 1, 1, 2,
       2, 2, 2, 1, 2, 2, 2, 1, 1, 2, 1, 2, 2, 1, 1, 2, 1, 1, 2, 2, 2, 1,
       1, 1, 2, 1, 2, 2, 1, 1, 1, 1, 1, 1, 2, 2, 1, 1, 1, 2, 2, 2, 2, 1,
       1, 2, 1, 1, 2, 1, 2, 2, 2, 1, 2, 2, 1, 2, 1, 1, 2, 1, 2, 1, 1, 1,
       2, 2, 1, 1, 2, 2, 1, 1, 2, 2, 2, 2, 2, 2, 2, 1, 1, 1, 2, 2, 2, 2,
       2, 1, 1, 1, 1, 2, 1, 1, 2, 1, 2, 1, 1, 2, 1, 1, 1, 1, 1, 1, 2, 1,
       1, 1, 1, 1, 1, 1, 2, 2, 1, 1, 2, 2, 2, 2, 1, 1, 1, 2, 2, 1, 1, 1,
       1, 1, 1, 2, 2, 1, 2, 1, 2, 1, 1, 2, 2, 1, 1, 1, 1, 2, 2, 1, 1, 1,
       1, 2, 1, 2, 2, 1, 1, 1, 2, 2, 2, 1, 1, 2, 1, 1, 1, 2, 2, 2, 1, 2,
       1, 2, 1, 1, 1, 2, 1, 2, 1, 1, 1, 2, 1, 1, 2, 1, 1, 2, 2, 2, 1, 2,
       2, 2, 2, 2, 1, 1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 1, 1, 2, 1, 1, 2, 1,
       1, 1, 2, 2, 2, 1, 1, 1, 2, 1, 1, 2, 1, 2, 2, 1], dtype=int64)

RFC_test_preds = RFC_model.predict(x_test)
RFC_test_preds

array([1, 2, 2, 1, 2, 2, 2, 1, 1, 1, 1, 1, 1, 1, 1, 2, 2, 1, 1, 1, 1, 2,
       2, 2, 1, 1, 1, 1, 2, 2, 1, 2, 1, 1, 2, 1, 2, 2, 1, 2, 2, 1, 2, 2,
       1, 1, 2, 2, 1, 1, 1, 1, 2, 2, 2, 2, 1, 1, 1, 1, 2, 1, 1, 2, 1, 1,
       1, 1, 2, 2, 2, 2, 2, 1, 1, 2, 1, 1, 1, 1, 2, 2, 2, 1, 1, 1, 1, 2,
       2, 1, 1, 1, 1, 2, 1, 1, 1, 2, 2, 1, 2, 1, 1, 2, 2, 2, 1, 1, 2, 2,
       1, 2, 1, 1, 1, 1, 2, 2, 1, 2, 1, 1, 1, 2, 2, 2, 2, 1, 2, 1, 2, 1,
       1, 2, 1, 1, 2, 2, 1, 1, 1, 1, 1, 2, 1, 1, 2, 1, 1, 1, 2, 2, 1, 2,
       2, 1, 1, 1, 2, 2, 2, 1, 1, 2, 2, 1, 1, 1, 2, 2, 2, 2, 1, 1, 2, 1,
       1, 2, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 2, 1, 1, 1,
       1, 2, 2, 1, 2, 2, 1, 1, 2, 1, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 2,
       2, 2, 2, 1, 1, 2, 1, 2, 2, 1, 2, 1, 2, 1, 1, 2, 1, 1, 2, 1, 1, 1,
       2, 2, 2, 2, 1, 1, 1, 2, 1, 2, 2, 1, 1, 2, 2, 2, 2, 1, 1, 1, 2, 2,
       1, 2, 1, 2, 2, 2, 2, 1], dtype=int64)

RFC_train_preds_score = RFC_model.score(x_train,y_train)
RFC_train_preds_score

1.0

RFC_test_preds_score = RFC_model.score(x_test,y_test)
RFC_test_preds_score

0.8602941176470589

# Compute the confusion matrix
conf_matrix_rfc = confusion_matrix(y_test, RFC_y_pred)

# Create a DataFrame for the confusion matrix (optional)
confusion_df_rfc = pd.DataFrame(conf_matrix_rfc, index=RFC_model.classes_, columns=RFC_model.classes_)

# Create a heatmap to visualize the confusion matrix
plt.figure(figsize=(8, 6))
sns.heatmap(confusion_df_rfc, annot=True, fmt='d', cmap='Blues', linewidths=.5, cbar=False)
plt.xlabel('Predicted')
plt.ylabel('Actual')
plt.title('Confusion Matrix')
plt.show()

# Evaluate the model
accuracy_rfc = accuracy_score(y_test, RFC_y_pred)
classification_rep_rfc = classification_report(y_test, RFC_y_pred)
confusion_mat_rfc = confusion_matrix(y_test, RFC_y_pred)

# Print the results
print(f"Accuracy: {accuracy_rfc}")
print("Classification Report:\n", classification_rep_rfc)
print("Confusion Matrix:\n", confusion_mat_rfc)

Accuracy: 0.8602941176470589
Classification Report:
               precision    recall  f1-score   support

           1       0.85      0.90      0.87       147
           2       0.87      0.82      0.84       125

    accuracy                           0.86       272
   macro avg       0.86      0.86      0.86       272
weighted avg       0.86      0.86      0.86       272

Confusion Matrix:
 [[132  15]
 [ 23 102]]